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Information processing device, information processing method, and recording medium

A technology of an information processing device and a registration unit, which is applied in the directions of reasoning methods, special data processing applications, machine learning, etc., and can solve the problems of failing to provide learning data for the dictionary, affecting the recognition accuracy of the dictionary, and optimizing the threshold value.

Active Publication Date: 2018-09-25
KK TOSHIBA +1
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0003] In semi-supervised learning, the threshold value used to determine the addition of untaught data to learning data greatly affects the recognition accuracy of the dictionary
However, in the prior art, the optimization of the threshold is not performed
Therefore, in the prior art, learning data for generating a dictionary with high recognition accuracy has not been provided.

Method used

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  • Information processing device, information processing method, and recording medium
  • Information processing device, information processing method, and recording medium
  • Information processing device, information processing method, and recording medium

Examples

Experimental program
Comparison scheme
Effect test

no. 1 approach

[0042] figure 1 It is a schematic diagram showing an example of the configuration of the information processing device 10 of this embodiment.

[0043] The information processing device 10 of this embodiment creates a dictionary using learning data (details will be described later). Furthermore, the information processing device 10 of the present embodiment attaches labels to untaught data by semi-supervised learning, and adds them to the learning data (details will be described later).

[0044] The information processing device 10 includes a processing unit 20 , a storage unit 22 , and an output unit 24 . The processing unit 20 , the storage unit 22 , and the output unit 24 are connected via the bus 9 .

[0045] The storage unit 22 stores various data. The storage unit 22 is, for example, an HDD (Hard Disk Drive), an optical disk, a memory card, a RAM (Random Access Memory, random access memory), or the like. In addition, the storage unit 22 may be configured to be install...

no. 2 approach

[0110] In the present embodiment, a method for reclassifying groups and correcting the additional taught data 34 in the learning data 30 will be described.

[0111] Figure 5 It is a schematic diagram showing an example of the configuration of the information processing device 10B of this embodiment. In addition, the same code|symbol is attached|subjected to the structure which exhibits the same function as the above-mentioned embodiment in some cases, and description is abbreviate|omitted.

[0112] The information processing device 10B includes a processing unit 25 , a storage unit 26 , and an output unit 24 . The processing unit 25 , the storage unit 26 , and the output unit 24 are connected via the bus 9 . The output unit 24 is the same as that of the first embodiment.

[0113] The storage unit 26 stores various data. Storage unit 26 stores dictionary 22A, learning data 30 , unused data 36 , and evaluation data 22D. In the present embodiment, storage unit 26 stores a p...

no. 3 approach

[0150] In this embodiment, an embodiment using N learning data 30 will be described.

[0151] Figure 7 It is a schematic diagram showing an example of the configuration of the information processing device 10C of this embodiment. In addition, the same code|symbol is attached|subjected to the structure which exhibits the same function as the above-mentioned embodiment in some cases, and description is abbreviate|omitted.

[0152] The information processing device 10C includes a processing unit 27 , a storage unit 28 , and an output unit 24 . The processing unit 27 , the storage unit 28 , and the output unit 24 are connected via the bus 9 . The output unit 24 is the same as that of the first embodiment.

[0153] The storage unit 28 stores various data. Storage unit 28 stores dictionary 22A, learning data 30 , and unused data 36 . In the present embodiment, the storage unit 28 stores N learning data 30 . N is an integer of 2 or more.

[0154] Each of the N learning data 3...

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Abstract

The invention relates to an information processing device, information processing method, and recording medium. According to an embodiment, an information processing device includes a classification unit, a calculation unit, a selection unit, and an allocation unit. The classification unit classifies unlabeled data into groups. The calculation unit calculates an evaluation value of each of the groups depending on label recognition accuracy of a group classifier for recognizing a label for unknown data, the group classifier being generated for each of the groups by using the unlabeled data belonging to the group. The selection unit selects the group based on the evaluation value. The allocation unit allocates a label corresponding to a correct label to the unlabeled data belonging to the selected group.

Description

technical field [0001] Embodiments relate to an information processing device, an information processing method, and a recording medium. Background technique [0002] There is known a method of creating a dictionary for pattern recognition by performing semi-supervised learning using data that has been taught and data that has not been taught. For example, there is known a method of updating a dictionary by using a dictionary learned from taught data to predict labels of untaught data, adding them to learning data, and repeating learning. At this time, a method is known in which not all untaught data is added to the learning data, but only data for which the reliability of the estimated label is equal to or greater than a threshold value is added to the learning data. [0003] In semi-supervised learning, the threshold value used to determine the addition of untaught data to learning data greatly affects the recognition accuracy of the dictionary. However, in the prior art...

Claims

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Application Information

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IPC IPC(8): G06K9/66G06K9/62G06N20/00
CPCG06N20/00G06V30/194G06F18/28G06F16/353G06N5/047G06N7/01
Inventor 田中辽平
Owner KK TOSHIBA
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