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Machine supervised learning method and device

A technology of supervised learning and machine learning models, applied in the field of machine supervised learning methods and devices, can solve the problems of uncontrollable training process and uncorrectable machine learning models, etc., and achieve the effect of accurate results

Pending Publication Date: 2022-05-10
深圳安巽科技有限公司
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  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

The existing machine supervised learning has the following technical problems: it can no longer correct the machine learning model according to the training results, the quality of the training results is completely determined by the training samples, and the training process cannot be controlled

Method used

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  • Machine supervised learning method and device

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Experimental program
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no. 1 approach

[0032] see figure 1 , the first embodiment of the machine supervised learning method of the present invention comprises the following steps:

[0033] Step S1: Manually judge multiple classification results of the machine learning model to create a black sample set and a white sample set.

[0034] Each sample data in the black sample set and the white sample set corresponds to a feature vector, and each feature vector includes multiple feature dimensions.

[0035] For example, machine supervised learning is used to predict and classify the behavior of the dialogue bill to determine whether the bill is a normal bill or an abnormal bill such as fraud. The black sample set includes, for example, multiple sample data that have been manually judged as abnormal bills, and the white sample set. For example, it includes a plurality of sample data that are judged to be normal by manual research.

[0036] Step S2: Perform sample collision on the black sample set and the white sample se...

no. 2 approach

[0041] see figure 2 , the second embodiment of the machine supervised learning method of the present invention comprises the following steps:

[0042] Step S1a: use the training sample set to perform machine learning to obtain a machine learning model.

[0043] Step S1b: Use the machine learning model to predict and classify the test sample set to obtain multiple classification results.

[0044] Step S1: Manually judge multiple classification results of the machine learning model to create a black sample set and a white sample set.

[0045] It should be understood that step S1 is to manually study and judge the multiple classification results of step S1b.

[0046] Step S2a: Manually set the corresponding machine weights for each feature dimension of the feature vector.

[0047] It should be understood that the initial machine weights of each feature dimension can be set through step S2a. For example, one sample data of the black sample set corresponds to a feature vector ...

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Abstract

The invention discloses a machine supervised learning method and device, and the method comprises the steps: S1, carrying out the manual research and judgment of a plurality of classification results of a machine learning model, so as to create a black sample set and a white sample set; s2, performing sample collision on the black sample set and the white sample set to modify the machine weight of each feature dimension; and S3, optimizing the machine learning model by using the black sample set and the white sample set after sample collision. According to the method, human intervention labeling is carried out on the result of machine supervised learning through the black sample set and the white sample set, then the result after human intervention is collided, and the machine calculates a reasonable machine weight, so that the machine learning model is corrected and optimized according to the training result, the training process is effectively controlled, and the training efficiency is improved. And the result of machine supervised learning is more accurate.

Description

technical field [0001] The invention relates to the technical field of machine supervised learning, in particular to a machine supervised learning method and device. Background technique [0002] Machine supervised learning is the machine learning task of inferring a function from labeled training data. The training data consists of a set of training examples. In supervised learning, each training example is composed of metadata and training samples. The existing machine supervised learning has the following technical problems: it can no longer correct the machine learning model according to the training results, the quality of the training results is completely determined by the training samples, and the training process cannot be controlled. Contents of the invention [0003] (1) Solved technical problems [0004] Aiming at the deficiencies of the prior art, the present invention provides a machine supervised learning method and device, which can solve the above technic...

Claims

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Application Information

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IPC IPC(8): G06N20/00G06K9/62
CPCG06N20/00G06F18/41G06F18/24G06F18/214
Inventor 余为宾高磊
Owner 深圳安巽科技有限公司
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