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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  • 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

Method used

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  • Machine supervised learning method and device

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Experimental program
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Example

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

[0033] Step S1: manually study and judge multiple classification results of machine learning model to create black sample set and 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, the machine supervised learning dialogue line is used for prediction and classification to judge whether the bill is a normal bill or an abnormal bill such as fraud. For example, the black sample set includes multiple sample data judged as abnormal bills manually, and the white sample set includes multiple sample data judged as normal bills manually.

[0036] Step S2: conduct sample collision on the black sample set and the white sample set to modify the machine weight of each feature dimension.

[0037]Sample...

Example

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

[0042] Step S1A: use the training sample set for machine learning to obtain the machine learning model.

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

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

[0045] It should be understood that step S1 is a manual study and judgment of multiple classification results in step S1b.

[0046] Step S2A: manually set the corresponding machine weight of each feature dimension of the feature vector.

[0047] It should be understood that the initial machine weight of each feature dimension is set through step S2A. For example, a sample data of the black sample set corresponds to a feature vector A1 = {A1, A2, A3, A4}, ...

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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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