Learning Device

Abstract

A learning device 400 comprises a reception unit 421 that receives a learner from another learning device, and a generation unit 422 that generates an adapted learner so as to execute appropriate prediction for data in the learning device 400 by using the learner received by the reception unit 421 and the data in the learning device 400.

Description

[Technical Field]

[0001] The present invention relates to a learning device, a learning method, a recording medium, and an inference device. [Background technology]

[0002] In order to achieve better performance, the learners trained by each participant may be combined.

[0003] For example, Non-Patent Document 1 describes a technique called Gradient Boosting Forest (GBF), in which participants each create a decision tree at each step and combine the created decision trees to create a model with better performance.

[0004] Further, as a related document, for example, there is Patent Document 1. Patent Document 1 describes GBDT (Gradient Boosting Decision Tree) and the like. [Prior art documents] [Patent documents]

[0005] [Patent Document 1] Japanese Patent Publication No. 2021-140296 [Non-patent literature]

[0006] [Non-Patent Document 1] Feng Wang et al., Gradient Boosting Forest: a Two-Stage Ensemble Method Enabling Federated Learning of GBDTs, ICONIP 2021: Neural Information Processing, pp75-86 [Retrieved March 8, 2022], Internet<https: / / link.springer.com / chapter / 10.1007 / 978-3-030-92270-2_7> Summary of the Invention [Problem to be solved by the invention]

[0007] In the case of the technology described in Non-Patent Document 1, the learning devices trained by each participant are combined. Therefore, the ultimately created learning device corresponds to, for example, the training data possessed by each participant. On the other hand, for example, in cases where the training data possessed by each participant has a biased distribution, it may be desirable to prepare a learning device that is more suited to a specific individual while improving performance. However, it has been difficult to deal with such cases by simply combining learning devices as described in Non-Patent Document 1.

[0008] SUMMARY OF THE INVENTION It is therefore an object of the present invention to provide a learning device, a learning method, and a recording medium that can solve the above-mentioned problems. [Means for solving the problem]

[0009] In order to achieve this object, a learning device according to one embodiment of the present disclosure includes: a receiving unit that receives a learning device from another learning device; a generation unit that generates an appropriate learning unit using the learning unit received by the receiving unit and data stored in the device itself to perform prediction appropriate for the data stored in the device itself; have The structure is as follows.

[0010] Furthermore, a learning method according to another aspect of the present disclosure includes: The information processing device receiving a learner from another learner; Using the received learning device and the data stored in the device, an appropriate learning device is generated to perform predictions appropriate for the data stored in the device. The structure is as follows.

[0011] Furthermore, a recording medium according to another aspect of the present disclosure includes: In the information processing device, receiving a learner from another learner; Using the received learning device and the data stored in the device, an appropriate learning device is generated to perform predictions appropriate for the data stored in the device. It is a computer-readable recording medium that records a program for implementing the processing.

[0012] Furthermore, an inference device according to another aspect of the present disclosure includes: An inference device that infers a label for an input of a feature, a storage device that stores the learning devices received from other learning devices and the coupling coefficients calculated using the data stored in the learning device itself so as to perform predictions suitable for the data stored in the learning device itself; an inference unit that performs inference using the learning devices and combination coefficients stored in the storage device in response to input of feature quantities; have The structure is as follows. [Effects of the Invention]

[0013] According to the above-described configurations, it is possible to improve performance and prepare a learning device that is more suited to a specific individual. [Brief explanation of the drawings]

[0014] [Figure 1] FIG. 1 is a diagram for explaining an overview of the present disclosure. [Figure 2] 1 is a diagram illustrating a configuration example of a learning system according to a first embodiment of the present disclosure. [Figure 3] FIG. 2 is a block diagram illustrating an example of the configuration of a learning device. [Figure 4] FIG. 10 is a diagram illustrating an example of learning data. [Figure 5] FIG. 10 is a diagram illustrating an example of validation data. [Figure 6] FIG. 10 is a diagram illustrating an example of an algorithm. [Figure 7] FIG. 10 is a diagram illustrating another example of an algorithm. [Figure 8] 10 is a flowchart illustrating an example of the operation of the learning device. [Figure 9]FIG. 10 is a diagram illustrating an example of a hardware configuration of a learning device according to a second embodiment of the present disclosure. [Figure 10] 1 is a block diagram showing an example of the configuration of a learning device; [Figure 11] FIG. 2 is a block diagram showing an example of the configuration of an inference device. DETAILED DESCRIPTION OF THE INVENTION

[0015] [First embodiment] A first embodiment of the present disclosure will be described with reference to FIGS. 1 to 8. FIG. 1 is a diagram for explaining an overview of the present disclosure. FIG. 2 is a diagram illustrating an example configuration of a learning system 100. FIG. 3 is a block diagram illustrating an example configuration of a learning device 300. FIG. 4 is a diagram illustrating an example of learning data information 341. FIG. 5 is a diagram illustrating an example of validation data information 342. FIGS. 6 and 7 are diagrams illustrating an example of an algorithm performed in the learning system 100. FIG. 8 is a flowchart illustrating an example operation of the learning device 300.

[0016] In a first embodiment of the present disclosure, as shown in FIG. 1, a learning system 100 including a learning device 300 that generates a combined learning device to perform more appropriate predictions for data such as training data and validation data held by the learning device itself, based on a learning device received from another participant in the learning system 100 and a learning device trained by the learning device itself. As will be described later, when the learning device 300 described in this embodiment receives a decision tree that is a learning device from another participant in the learning system 100, the learning device 300 calculates new features by inputting the training data held by the learning device itself into the received decision tree. Then, the learning device 300 generates a decision tree that is a new learning device for the learning device itself, based on the training data and the calculated features.

[0017] Furthermore, the learning device 300 calculates a combining coefficient corresponding to each decision tree using the generated decision tree, decision trees received from other participants, and pre-stored validation data. For example, the learning device 300 calculates a combining coefficient for each decision tree based on the decision tree and the validation data so as to optimize the prediction performance for its own validation data. Thereafter, the learning device 300 generates a new combined decision tree by combining the decision trees received from other learning devices 200 or the generated decision tree with the past combined decision tree using the calculated combining coefficient. In other words, the learning device 300 generates a combined decision tree, which is an appropriated learner, by performing combination using the combining coefficient.

[0018] 1, the learning system 100 described in this embodiment can repeat the above-described process. That is, the learning system 100 can repeat a series of steps, including another learning device 200 generating a decision tree, learning device 300 generating a decision tree using the decision tree generated by the other learning device 200, calculating the combining coefficient, and combining the decision trees, multiple times until, for example, a predetermined condition is satisfied.

[0019] The training data includes multiple features such as gender, age, height, weight, etc., and labels indicating whether or not the data is sick. Features may also be referred to as explanatory variables or attributes. Labels may also be referred to as objective variables. Specific examples of features and labels may be other than those listed above. Validation data refers to data used for validation that can be used, for example, when evaluating a learning device. Similar to training data, validation data includes multiple features and labels. A decision tree refers to a model that is trained by repeatedly sorting input data using a binary tree based on conditional branching of the features until the explanatory performance of the labels is sufficiently improved. Decision trees include regression trees for regression tasks and classification trees for classification tasks.

[0020] In addition, in this embodiment, a case where a decision tree is used as a learning device will be described. However, the learning device that is the subject of the present invention is not limited to a decision tree. For example, each participant in the learning system 100 may generate a shallow neural network, a support vector machine, or the like as a learning device. Even when each participant in the learning system 100 generates a neural network, a support vector machine, or the like as a learning device, the present invention can be applied without any problems.

[0021] Fig. 2 shows an example of the overall configuration of learning system 100. Referring to Fig. 2, learning system 100 has one or more other learning devices 200 and learning device 300. As shown in Fig. 2, other learning devices 200 and learning device 300 are connected via a network or the like so that they can communicate with each other.

[0022] Other learning device 200 is an information processing device that generates a decision tree, which is a learner, by performing learning based on training data held by other learning device 200. In addition, other learning device 200 can transmit the generated decision tree to other other learning devices 200 and learning device 300.

[0023] Furthermore, the other learning device 200 may be configured to combine decision trees, which are learning units received from other learning devices 200, learning devices 300, etc., using a predetermined coefficient or the like. Here, the predetermined coefficient can be determined in advance, for example, based on the amount of data held by each participant, such as the other learning devices 200 and learning devices 300, in the learning system 100. For example, the predetermined coefficient may be calculated by dividing the amount of training data held by the own device by the sum of the amounts of training data held by the other learning devices 200 and learning devices 300 in the learning system 100.

[0024] For example, as described above, the other learning device 200 generates a decision tree, which is a learner, based on training data held by the device itself, and transmits the generated decision tree to other information processing devices, etc. in the learning system 100. The other learning device 200 can also receive decision trees from other information processing devices, etc. in the learning system 100, and combine the received decision trees using predetermined coefficients, etc. For example, the other learning device 200 may realize each of the above processes using a method described in Non-Patent Document 1, etc.

[0025] The learning device 300 is an information processing device that generates a combined decision tree, which is a combined learner, so as to make more appropriate predictions for data such as learning data and validation data that the device itself holds. FIG. 3 shows an example configuration of the learning device 300. Referring to FIG. 3, the learning device 300 has, as main components, for example, an operation input unit 310, a screen display unit 320, a communication I / F unit 330, a storage unit 340, and a calculation processing unit 350.

[0026] 3 illustrates an example in which the functions of learning device 300 are realized using a single information processing device. However, learning device 300 may be realized using multiple information processing devices, for example, on the cloud. Furthermore, learning device 300 may not include some of the components illustrated above, such as not having operation input unit 310 or screen display unit 320, or may have components other than those illustrated above.

[0027] Operation input unit 310 is made up of operation input devices such as a keyboard, a mouse, etc. Operation input unit 310 detects operations by the operator operating learning device 300 and outputs the operations to calculation processing unit 350.

[0028] The screen display unit 320 is composed of a screen display device such as an LCD (Liquid Crystal Display). The screen display unit 320 can display various information stored in the storage unit 340 on the screen in response to instructions from the arithmetic processing unit 350.

[0029] The communication I / F unit 330 is composed of a data communication circuit etc. The communication I / F unit 330 performs data communication with an external device connected via a communication line.

[0030] The storage unit 340 is a storage device such as a hard disk or memory. The storage unit 340 stores processing information and a program 345 required for various processes in the arithmetic processing unit 350. The program 345 is read into the arithmetic processing unit 350 and executed to realize various processing units. The program 345 is read in advance from an external device or recording medium via a data input / output function such as the communication I / F unit 330, and is stored in the storage unit 340. Main information stored in the storage unit 340 includes, for example, learning data information 341, validation data information 342, learning device information 343, coefficient information 344, etc.

[0031] The learning data information 341 includes learning data used when learning a decision tree, which is a learning device. For example, the learning data information 341 is acquired in advance by a method such as acquiring it from an external device via the communication I / F unit 330 or inputting it using the operation input unit 310, and is stored in the storage unit 340.

[0032] FIG. 4 shows an example of the training data information 341. Referring to FIG. 4, in the training data information 341, a plurality of feature quantities are associated with labels. For example, in the example shown in FIG. 4, the feature quantities (x1, x2, ..., x d ) is associated with the label y1. As shown in FIG. 4, the learning data information 341 may include multiple pieces of learning data.

[0033] The validation data information 342 includes validation data that is data used when verifying the performance of a decision tree, etc. For example, the validation data information 342 is acquired in advance by a method such as acquiring it from an external device via the communication I / F unit 330 or inputting it using the operation input unit 310, and is stored in the storage unit 340.

[0034] FIG. 5 shows an example of the validation data information 342. Referring to FIG. 5, in the validation data information 342, similar to the learning data information 341, a plurality of feature quantities and labels are associated with each other. For example, in the example shown in FIG. 5, the feature quantity (x 11 , x 12 , …, x 1d ) and label y 10 As shown in Fig. 5, the validation data information 342 may include multiple validation data.

[0035] The learning device information 343 includes information indicating decision trees received from other learning devices 200 and combined decision trees combined by the combining unit 355 (described later). For example, in the learning device information 343, a decision tree is associated with identification information indicating the sender of the decision tree. The learning device information 343 may include decision trees and combined decision trees for each step. For example, the learning device information 343 is updated when the receiving unit 351 (described later) receives a decision tree from another learning device 200, when the combining unit 355 generates a combined decision tree, and so on.

[0036] The coefficient information 344 includes a combination coefficient corresponding to each decision tree, such as a decision tree received from another learning device 200 or a decision tree generated by a learning unit 353 (described later). For example, the coefficient information 344 associates the identification information of the decision tree with the combination coefficient. The coefficient information 344 may include a combination coefficient for each step and each decision tree. For example, the coefficient information 344 is updated in response to the calculation of the combination coefficient by a coefficient calculation unit 354 (described later).

[0037] The arithmetic processing unit 350 has an arithmetic device such as a CPU (Central Processing Unit) and its peripheral circuits. The arithmetic processing unit 350 reads and executes a program 345 from the storage unit 340, thereby causing the above hardware and the program 345 to work together to realize various processing units. Major processing units realized by the arithmetic processing unit 350 include, for example, a receiving unit 351, a feature addition calculation unit 352, a learning unit 353, a coefficient calculation unit 354, a combining unit 355, an inference unit 356, and an output unit 357.

[0038] The receiving unit 351 receives a decision tree, which is a learning device, from another learning device 200. For example, the receiving unit 351 can receive a learning device from each other learning device 200 included in the learning system 100 at each step. The receiving unit 351 also stores the received decision tree in the storage unit 340 as learning device information 343.

[0039] Note that the receiving unit 351 may receive, for example, information indicating a difference from the decision tree in the previous step from another learning device 200. In this case, the receiving unit 351 may be configured to update the corresponding decision tree based on the received information indicating the difference.

[0040] The feature addition calculation unit 352 calculates additional training data based on the decision tree received by the receiving unit 351 and the training data included in the training data information 341. For example, the feature addition calculation unit 352 obtains output from the learning device by inputting each piece of training data included in the training data information 341 into the decision tree received by the receiving unit 351. The feature addition calculation unit 352 can acquire the output as an additional feature.

[0041] For example, the learning data information 341 contains the feature x i , label y i Contains (x i , y i ) (where i can be any value). Also, suppose that decision trees f1(), f2(), ... are received from another learning device 200. In this case, the feature addition calculation unit 352 inputs the feature xi into each decision tree to calculate f1(x i ), f2(x i As a result, the learning data to be learned by the learning unit 353 (described later) is calculated as (x i , f1(x i ), f2(x i ), …, y i )

[0042] For example, the feature addition calculation unit 352 may perform the above-described process for each decision tree and for each piece of training data. For example, the feature addition calculation unit 352 may perform the above-described process for each decision tree extracted by any method.

[0043] The learning unit 353 generates a decision tree, which is a learning device, by performing learning based on the feature calculated by the feature addition calculation unit 352 and the learning data indicated by the learning data information 341. The learning unit 353 also stores the generated decision tree in the storage unit 340 as learning device information 343.

[0044] For example, as described above, the additional feature amount is calculated by the feature amount addition calculation unit 352. Then, the learning unit 353 calculates (x i , f1(x i ), f2(x i ), …, y i ) and generate a decision tree by performing machine learning using training data that includes additional features.

[0045] The learning unit 353 may perform machine learning by adding the additional feature calculated by the feature addition calculation unit 352 to the learning data as is as described above, or may be configured to perform machine learning by adding, for example, a result of linearly combining the additional feature calculated by the feature addition calculation unit 352 to the learning data. The learning unit 353 may perform machine learning by adding both the additional feature calculated by the feature addition calculation unit 352 and the result of linearly combining the additional feature to the learning data.

[0046] The coefficient calculation unit 354 calculates a combining coefficient for each decision tree using the validation data indicated by the validation data information 342. For example, the coefficient calculation unit 354 calculates the combining coefficient so as to optimize the prediction performance for the validation data indicated by the validation data information 342. The coefficient calculation unit 354 can calculate the combining coefficient for each decision tree received by the receiving unit 351 or each decision tree generated by the learning unit 353. In addition, the coefficient calculation unit 354 stores the calculated combining coefficient in the storage unit 340 as coefficient information 344.

[0047] For example, the validation data information 342 includes the feature x 1i 、 Label y 1i Contains (x 1i ,y 1i ) is included in the validation data. 11 (), f 12 Assume that decision trees such as (), ... are received or generated. In this case, the coefficient calculation unit 354 first inputs validation data to each decision tree to obtain an output. For example, the coefficient calculation unit 354 inputs validation data (x 1i ,y 1i ) into a decision tree f 11 Input to () to output u i Furthermore, the coefficient calculation unit 354 calculates the validation data (x 1i ,y 1i ) into a decision tree f 12 Input to () to output v i Then, the coefficient calculation unit 354 obtains (u i , v i , y 1i ) to calculate the combination coefficient for each decision tree. For example, the coefficient calculation unit 354 may calculate the combination coefficient by performing linear regression. For example, the coefficient calculation unit 354 may calculate the combination coefficient by using the validation data (x 1i ,y 1i ) and output u i The coupling coefficients corresponding to the decision tree f11() may be determined by performing linear regression using

[0048] Specifically, for example, the coefficient calculation unit 354 calculates the validation data (x 1i ,y 1i ) and u i , v i By performing linear regression on Equation 1 using i can be determined.

number

[0049] The coefficient calculation unit 354 may calculate the coupling coefficients using the entire validation data, or may calculate the coupling coefficients using a portion of the validation data. For example, by referring to model information about the decision tree generated by the learning unit 353, such as the model structure and branching conditions, it is possible to identify the leaf nodes at which each validation data falls. Therefore, the coefficient calculation unit 354 may calculate the coupling coefficients for each leaf node, for example, by performing linear regression using the validation data for each leaf node. Even when calculating the coupling coefficients for each leaf node, a coupled decision tree can be generated in the same manner as described above by coupling each leaf node. When the coefficient calculation unit 354 calculates the coupling coefficients using the entire validation data, it can also be said that the coupling coefficients for the entire decision tree are calculated.

[0050] Furthermore, the coefficient calculation unit 354 may calculate the combining coefficient using a method other than the above examples. For example, the coefficient calculation unit 354 may calculate the combining coefficient using the learning data indicated by the learning data information 341 instead of the validation data. However, from the viewpoint of suppressing excessive bias, it is more desirable to calculate the combining coefficient using the validation data rather than using the learning data. The coefficient calculation unit 354 may calculate the combining coefficient using any other method.

[0051] The combining unit 355 generates a new combined decision tree by combining the decision tree received by the receiving unit 351 or the decision tree generated by the learning unit 353 with the combined decision tree of the previous step included in the learning device information 343, using the combining coefficient calculated by the coefficient calculation unit 354. The combining unit 355 also stores the newly generated combined decision tree in the storage unit 340 as the learning device information 343.

[0052] For example, the combining unit 355 performs combining using a combining coefficient by solving the following equation (2).

number

[0053] The inference unit 356 performs inference using the combined decision tree. For example, the inference unit 356 can perform inference using the latest combined decision tree.

[0054] As shown in Equation 3, the latest combined decision tree f final The ( ) includes the decision trees, connection coefficients, etc. received or generated in the past steps. Therefore, it can be said that the inference unit 356 performs inference using the decision trees, connection coefficients, etc. generated in the past steps.

number

number

number

[0055] The output unit 357 outputs the decision tree and the combined decision tree, and outputs the inference result by the inference unit 356.

[0056] For example, the output unit 357 can transmit the decision tree generated by the learning unit 353 or the combined decision tree generated by the combining unit 355 to an external device such as another learning device 200 via the communication I / F unit 330. The output unit 357 may perform output at any timing, such as after processing by the learning unit 353 or the combining unit 355.

[0057] Furthermore, the output unit 357 can display the result of the inference by the inference unit 356 on the screen display unit 320 and can transmit it to an external device via the communication I / F unit 330.

[0058] The above is an example of the configuration of the learning device 300. Note that FIG. 6 shows an example of the algorithm of the learning system 100 when dealing with a regression task, as described in Non-Patent Document 1. The example shown in FIG. 6 illustrates a case where participants such as other learning devices 200 and learning devices 300 are included in the K-class learning system 100. As illustrated in FIG. 6, in the learning system 100, the decision tree f generated by each participant is k () and so on are combined using a combination coefficient a. As described above, the combination coefficient a is calculated so as to optimize the prediction performance for the validation data. Therefore, by combining using the combination coefficient a, it is possible to improve performance and generate a combined decision tree in a form that is more suitable for the learning device 300 that has validation data.

[0059] Note that the example shown in FIG. 6 illustrates an example algorithm of the learning system 100 when a regression task is handled. However, even when a classification task is handled instead of a regression task, the combination using the combination coefficient a is performed as in the case of a regression task. Therefore, even when a classification task is handled, by performing the combination using the combination coefficient a as in the case of a regression task, it is possible to improve performance and generate a combined decision tree in a form more suitable for the learning device 300 having validation data. For example, Non-Patent Document 1 describes an example algorithm when a classification task is handled, as shown in FIG. 7. Referring to FIG. 7, it can be seen that even when a classification task is handled, the combination using the combination coefficient a is performed as in the case of a regression task.

[0060] Next, an example of the operation of the learning device 300 will be described with reference to Fig. 8. Fig. 8 is a flowchart showing an example of the operation of the learning device 300. Referring to Fig. 8, the receiving unit 351 receives a decision tree, which is a learning unit, from another learning device 200 (step S101).

[0061] The feature addition calculation unit 352 calculates additional training data based on the decision tree received by the receiving unit 351 and the training data included in the training data information 341 (step S102). For example, the feature addition calculation unit 352 obtains output from the learning device by inputting each piece of training data included in the training data information 341 into the decision tree received by the receiving unit 351. The feature addition calculation unit 352 can acquire the output as an additional feature.

[0062] The learning unit 353 generates a decision tree, which is a learning device, by performing learning based on the features calculated by the feature addition calculation unit 352 and the learning data indicated by the learning data information 341 (step S103). That is, the learning unit 353 generates a decision tree by performing learning by adding the features calculated by the feature addition calculation unit 352 to the learning data indicated by the learning data information 341.

[0063] The coefficient calculation unit 354 calculates a combining coefficient for each decision tree using the validation data indicated by the validation data information 342 (step S104). For example, the coefficient calculation unit 354 calculates the combining coefficient so as to optimize the prediction performance for the validation data indicated by the validation data information 342. The coefficient calculation unit 354 can calculate the combining coefficient for each decision tree received by the receiving unit 351 or generated by the learning unit 353.

[0064] The combining unit 355 uses the combining coefficient calculated by the coefficient calculation unit 354 to combine the decision tree received by the receiving unit 351 or the decision tree generated by the learning unit 353 with the combined decision tree of the previous step included in the learning device information 343, thereby generating a new combined decision tree (step S105).

[0065] The above is an example of the operation of the learning device 300.

[0066] As described above, the learning device 300 includes the feature addition calculation unit 352 and the learning unit 353. With this configuration, the learning unit 353 can generate a decision tree that is a learning device by performing learning using learning data to which the feature calculated by the feature addition calculation unit 352 has been added. As a result, a decision tree can be generated that incorporates the results of learning by the other learning device 200. This makes it possible to improve performance and generate a decision tree that is a learning device that is more suitable for the data stored in the device itself.

[0067] Furthermore, the learning device 300 includes a coefficient calculation unit 354 and a combining unit 355. With this configuration, the combining unit 355 can calculate each decision tree using the combining coefficients calculated by the coefficient calculation unit 354. As described above, the combining coefficients are calculated so as to optimize the prediction performance for the validation data. Therefore, by combining each decision tree using the combining coefficients, it is possible to improve performance and generate a decision tree that is a learner more suitable for the learning device 300 that has validation data.

[0068] In the present embodiment, the learning device 300 includes both the feature amount addition calculation unit 352 and the coefficient calculation unit 354. However, the learning device 300 may include only one of the feature amount addition calculation unit 352 and the coefficient calculation unit 354.

[0069] For example, if the learning device 300 does not have the feature addition calculation unit 352, the learning unit 353 generates a decision tree by performing learning based on the learning data included in the learning data information 341. Even in such a case, as described above, the combination coefficients are calculated to optimize the prediction performance for the validation data, and therefore, by combining the decision trees using the combination coefficients, it is possible to generate a decision tree that is a learner that is more suitable for the learning device 300 that has the validation data while improving performance.

[0070] Furthermore, for example, if the learning device 300 does not have the coefficient calculation unit 354, the combining unit 355 combines a decision tree generated by incorporating the results of learning by the other learning device 200 with the combined decision tree of the previous step. As a result, it is possible to generate a decision tree that is a learner that is more suitable for the data stored in the device itself while improving performance. Note that if the learning device 300 does not have the coefficient calculation unit 354, the combining unit 355 may combine a decision tree generated without using a combining coefficient with the combined decision tree of the previous step.

[0071] As described above, in this embodiment, a case has been described in which a decision tree is used as a learner. However, the learner of the present invention is not limited to a decision tree. For example, the learner may be a shallow neural network or a support vector machine. Of course, the finally generated learner may also be one corresponding to each of the above learners. For example, when a decision tree is used as a learner, the finally generated combined decision tree is a GBDT (Gradient Boosting Decision Tree) model. Furthermore, when a neural network is used as a learner, the finally generated model is a Gradient Boosting Neural Network.

[0072] [Second embodiment] Next, a second embodiment of the present disclosure will be described with reference to Fig. 9 to Fig. 11. Fig. 9 is a diagram illustrating an example of the hardware configuration of a learning device 400. Fig. 10 is a block diagram illustrating an example of the configuration of a learning device 400. Fig. 11 is a block diagram illustrating an example of the configuration of an inference device 500.

[0073] In the second embodiment of the present disclosure, a configuration example of a learning device 400 will be described, which is an information processing device that receives learning devices from other devices and combines the learning devices to perform optimal predictions for its own data. Fig. 9 shows an example of the hardware configuration of the learning device 400. Referring to Fig. 9, the learning device 400 has, as an example, the following hardware configuration. ·CPU(Central Processing Unit)401(Arithmetic unit) ROM (Read Only Memory) 402 (storage device) RAM (Random Access Memory) 403 (storage device) Programs 404 loaded into RAM 403 A storage device 405 for storing the program group 404 A drive device 406 that reads and writes data from a recording medium 410 outside the information processing device A communication interface 407 for connecting to a communication network 411 outside the information processing device Input / output interface 408 for inputting and outputting data Bus 409 connecting each component

[0074] 10 by the CPU 401 acquiring and executing the program group 404. The program group 404 is stored in advance in, for example, the storage device 405 or the ROM 402, and is loaded into the RAM 403 or the like by the CPU 401 for execution as needed. The program group 404 may be supplied to the CPU 401 via the communication network 411, or may be stored in advance in the recording medium 410, with the drive device 406 reading out the programs and supplying them to the CPU 401.

[0075] 9 shows an example of the hardware configuration of the learning device 400. The hardware configuration of the learning device 400 is not limited to the above. For example, the learning device 400 may be configured with only a part of the above configuration, such as excluding the drive device 406.

[0076] The receiving unit 421 receives a learning device from another learning device.

[0077] The generation unit 422 uses the learning devices received by the receiving unit 421 and data held by the device itself to generate an appropriate learning device to perform predictions appropriate for the data held by the device itself. For example, the generation unit 422 generates an appropriate learning device to perform predictions appropriate for the data held by the device itself by combining the learning devices received by the receiving unit 421 using a combination coefficient calculated using the data held by the device itself. Alternatively, the generation unit 422 generates an appropriate learning device by generating a learning device using learning data to which additional features calculated using the learning devices received by the receiving unit are added. For example, the generation unit 422 can generate an appropriate learning device by performing any of the methods exemplified above or a combination thereof.

[0078] As described above, the learning device 400 includes a receiving unit 421 and a generating unit 422. With this configuration, the generating unit 422 can generate an appropriate learning device that performs predictions appropriate for the data stored in the device itself, using the learning device received by the receiving unit 421 and data stored in the device itself. As a result, it is possible to generate a learning device that is more appropriate for the data stored in the device itself, while improving performance.

[0079] The above-described learning device 400 can be realized by incorporating a predetermined program into an information processing device such as the learning device 400. Specifically, a program according to another aspect of the present invention is a program for realizing a process in which an information processing device such as the learning device 400 receives a learning device from another learning device, and uses the received learning device and data stored in the device to generate an appropriate learning device that performs predictions appropriate for the data stored in the device.

[0080] Furthermore, a learning method executed by an information processing device such as the above-mentioned learning device 400 is a method in which an information processing device such as the learning device 400 receives a learning device from another learning device, and uses the received learning device and data held by the device itself to generate an appropriate learning device that makes predictions appropriate for the data held by the device itself.

[0081] Even if the invention is a program having the above-mentioned configuration, or a computer-readable recording medium having the program recorded thereon, or a learning method, it can achieve the same functions and effects as the above-mentioned learning device 400, and therefore can achieve the above-mentioned objective of the present disclosure.

[0082] Furthermore, the object of the present disclosure can be similarly achieved by an inference device 500 or the like that performs inference using an appropriated learning device generated by the above-described learning device 400 or the like. For example, as shown in FIG. 11 , the inference device 500 includes a storage device 521 that stores a learning device received from another learning device and a combination coefficient calculated using data stored in the device itself to perform a prediction appropriate for the data stored in the device itself, and an inference unit 522 that performs inference using the learning device and combination coefficient stored in the storage device in response to input of feature quantities. Note that the hardware configuration of the inference device 500 may be the same as that of the learning device 400.

[0083] <Additional Notes> A part or all of the above-described embodiments can be described as follows: The learning device and the like according to the present invention will be outlined below. However, the present invention is not limited to the following configuration.

[0084] (Appendix 1) a receiving unit that receives a learning device from another learning device; a generation unit that generates an appropriate learning unit using the learning unit received by the receiving unit and data stored in the device itself to perform prediction appropriate for the data stored in the device itself; have Learning device. (Appendix 2) 10. The learning device according to claim 1, a calculation unit that calculates a coupling coefficient using data stored in the device itself so as to perform a prediction suitable for the data; The generation unit generates the appropriate learning unit so as to perform prediction suitable for data stored in the device by combining the learning units received by the reception unit using the combination coefficient calculated by the calculation unit. Learning device. (Appendix 3) 3. The learning device according to claim 2, The calculation unit performs linear regression using the results of inputting data held by the device itself into a learning device, thereby calculating a coupling coefficient so as to perform prediction suitable for the data. Learning device. (Appendix 4) 10. The learning device according to claim 2 or 3, The calculation unit calculates the coupling coefficient using validation data that is data for validation. Learning device. (Appendix 5) A learning device according to any one of Supplementary Note 2 to Supplementary Note 4, The calculation unit identifies data that falls at each leaf node in a decision tree that is a learning device, and calculates the coupling coefficient using the data for each leaf node. Learning device. (Appendix 6) 10. The learning device according to claim 1, wherein: a feature calculation unit that calculates additional features using the learning device and training data received by the receiving unit; a learning unit that generates a learning device by performing learning by adding the feature calculated by the feature calculation unit to the training data; and The generation unit generates the appropriate learning device using the learning device generated by the learning unit. Learning device. (Appendix 7) 7. The learning device according to claim 6, a calculation unit that calculates a coupling coefficient corresponding to the learning device received by the receiving unit using data stored in the device itself; The generation unit generates the appropriate learning device by combining the learning device received by the reception unit and the learning device generated by the learning unit using the combination coefficient calculated by the calculation unit. Learning device. (Appendix 8) The information processing device receiving a learner from another learner; Using the received learning device and the data stored in the device, an appropriate learning device is generated to perform predictions appropriate for the data stored in the device. How to learn. (Appendix 9) In the information processing device, receiving a learner from another learner; Using the received learning device and the data stored in the device, an appropriate learning device is generated to perform predictions appropriate for the data stored in the device. A computer-readable recording medium that records a program for implementing processing. (Appendix 10) An inference device that infers a label for an input of a feature, a storage device that stores the learning devices received from other learning devices and the coupling coefficients calculated using the data stored in the learning device itself so as to perform predictions suitable for the data stored in the learning device itself; an inference unit that performs inference using the learning devices and combination coefficients stored in the storage device in response to input of feature quantities; have Reasoning device.

[0085] Although the present invention has been described above with reference to the above-mentioned embodiments, the present invention is not limited to the above-mentioned embodiments. Various modifications that can be understood by those skilled in the art can be made to the configuration and details of the present invention within the scope of the present invention. [Explanation of symbols]

[0086] 100 Learning System 200 Other Learning Devices 300 Learning Device 310 Operation input section 320 Screen display section 330 Communication I / F section 340 Storage section 341 Learning Data Information 342 Validation Data Information 343 Learning Device Information 344 Coefficient Information 345 Programs 350 Processing Unit 351 Receiving Unit 352 Feature addition calculation unit 353 Learning Department 354 Coefficient calculation unit 355 Joint 356 Reasoning Department 357 Output Section 400 Learning Device 401 CPU 402 ROM 403 RAM 404 Programs 405 Storage device 406 Drive Unit 407 Communication Interface 408 Input / Output Interface 409 Bus 410 Recording Media 411 Communication Network 421 Receiving Unit 422 Generation part 500 Reasoning device 521 Storage device 522 Reasoning part

Claims

1. a receiving unit that receives a learning device from another learning device; a generation unit that generates an appropriate learning unit using the learning unit received by the receiving unit and data stored in the device itself to perform prediction appropriate for the data stored in the device itself; a calculation unit that calculates a coupling coefficient using data stored in the device itself so as to perform a prediction suitable for the data; and the generation unit combines the learning devices received by the reception unit using the combination coefficient calculated by the calculation unit to generate the appropriate learning device so as to perform prediction appropriate for data stored in the device itself; The calculation unit identifies data that falls at each leaf node in a decision tree that is a learning device, and calculates the coupling coefficient using the data for each leaf node. Learning device.

2. The learning device according to claim 1 , The calculation unit performs linear regression using the results of inputting data held by the device itself into a learning device, thereby calculating a coupling coefficient so as to perform prediction suitable for the data. Learning device.

3. 3. The learning device according to claim 1 or 2, The calculation unit calculates the coupling coefficient using validation data that is data for validation. Learning device.

4. The learning device according to any one of claims 1 to 3, a feature calculation unit that calculates additional features using the learning device and training data received by the receiving unit; a learning unit that generates a learning device by performing learning by adding the feature calculated by the feature calculation unit to the training data; and The generation unit generates the appropriate learning device using the learning device generated by the learning unit. Learning device.

5. The learning device according to claim 4, a calculation unit that calculates a coupling coefficient corresponding to the learning device received by the receiving unit using data stored in the device itself; The generation unit generates the appropriate learning device by combining the learning device received by the reception unit and the learning device generated by the learning unit using the combination coefficient calculated by the calculation unit. Learning device.

6. The information processing device receiving a learner from another learner; Using the received learning device and the data stored in the device, generate an appropriate learning device to perform predictions appropriate for the data stored in the device; When generating the appropriate learning device, a combination coefficient is calculated using data held by the device itself so as to make a prediction suitable for the data, and the calculated combination coefficient is used to combine the received learning devices, thereby generating the appropriate learning device so as to make a prediction suitable for the data held by the device itself; When calculating the coupling coefficients, data that falls into each leaf node in the decision tree, which is a learning device, is identified, and the coupling coefficients are calculated using the data for each leaf node. How to learn.

7. In the information processing device, receiving a learner from another learner; Using the received learning device and the data stored in the device, an appropriate learning device is generated to perform predictions appropriate for the data stored in the device. Realize the processing, When generating the appropriate learning device, a combination coefficient is calculated using data held by the device itself so as to make a prediction suitable for the data, and the calculated combination coefficient is used to combine the received learning devices, thereby generating the appropriate learning device so as to make a prediction suitable for the data held by the device itself; When calculating the coupling coefficients, data that falls into each leaf node in the decision tree, which is a learning device, is identified, and the coupling coefficients are calculated using the data for each leaf node. program.

8. A receiving unit that receives a learning device from another learning device; a generation unit that generates an appropriate learning unit using the learning unit received by the receiving unit and data stored in the device itself to perform prediction appropriate for the data stored in the device itself; a feature calculation unit that calculates additional features using the learning device and training data received by the receiving unit; a learning unit that generates a learning device by performing learning by adding the feature calculated by the feature calculation unit to the training data; and The generation unit generates the appropriate learning device using the learning device generated by the learning unit. Learning device.

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