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Hyper-parameter automatic optimization method and system of non-supervised machine learning

A machine learning and automatic optimization technology, applied in the computer field, can solve the problem of low optimization efficiency of hyperparameter optimization methods, and achieve the effect of reducing the complexity of use and high optimization efficiency.

Inactive Publication Date: 2018-05-22
TSINGHUA UNIV
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  • Abstract
  • Description
  • Claims
  • Application Information

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Problems solved by technology

[0006] The purpose of the present invention is to provide a hyperparameter automatic optimization method and system of unsupervised machine learning, which solves the technical problem of low optimization efficiency of the hyperparameter optimization method in the prior art

Method used

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Embodiment 1

[0036] figure 1 It is a schematic diagram of an automatic hyperparameter optimization method for unsupervised machine learning according to an embodiment of the present invention, such as figure 1 As shown, the embodiment of the present invention provides a hyperparameter automatic optimization method for unsupervised machine learning, including:

[0037] Step S10, determining the algorithm performance evaluation model of the unsupervised machine learning algorithm, the hyperparameters of the unsupervised machine learning algorithm, the search space of the hyperparameters, and the evaluation criteria for hyperparameter optimization according to the unsupervised machine learning algorithm ;

[0038] Step S20, determining the optimal value of the hyperparameter according to the algorithm performance evaluation model, the search space and the evaluation criterion.

[0039] Further, according to the unsupervised machine learning algorithm, the algorithm performance evaluation mo...

Embodiment 2

[0097] figure 2 It is a schematic diagram of an automatic hyperparameter optimization system for unsupervised machine learning according to an embodiment of the present invention, such as figure 2 As shown, the embodiment of the present invention provides a hyperparameter automatic optimization system of unsupervised machine learning, which is used to complete the method in the above embodiment, and specifically includes a query module 10 and an optimization module 20, wherein,

[0098] The query module 10 is used to determine the algorithm performance evaluation model of the unsupervised machine learning algorithm, the hyperparameters of the unsupervised machine learning algorithm, the search space of the hyperparameters and the optimization parameters of the hyperparameters according to the unsupervised machine learning algorithm. evaluation criteria;

[0099] The optimization module 20 is used to determine the optimal value of the hyperparameter according to the algorith...

Embodiment 3

[0102] image 3 A schematic structural diagram of an electronic device for automatically optimizing hyperparameters of unsupervised machine learning provided by an embodiment of the present invention, such as image 3 As shown, the device includes: a processor 801, a memory 802 and a bus 803;

[0103] Wherein, the processor 801 and the memory 802 complete mutual communication through the bus 803;

[0104] The processor 801 is used to call the program instructions in the memory 802 to execute the methods provided by the above method embodiments, including, for example:

[0105] Determine the algorithm performance evaluation model of the unsupervised machine learning algorithm, the hyperparameters of the unsupervised machine learning algorithm, the search space of the hyperparameters, and the evaluation criteria for hyperparameter optimization according to the unsupervised machine learning algorithm;

[0106] The optimal value of the hyperparameter is determined according to t...

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Abstract

The invention provides a hyper-parameter automatic optimization method and system of non-supervised machine learning. The hyper-parameter automatic optimization method of non-supervised machine learning includes the steps: according to the non-supervised machine learning algorithm, determining an algorithm performance assessment model of the non-supervised machine learning algorithm, the hyper-parameter of the non-supervised machine learning algorithm, the searching space of the hyper-parameter and the evaluation criterion of hyper-parameter optimization; and according to the algorithm performance assessment model, the searching space and the evaluation criterion, determining the optimal value of the hyper-parameter. The hyper-parameter automatic optimization method and system of non-supervised machine learning can deeply analyze the hyper-parameter problem in the non-supervised machine learning algorithm, can analyze the distribution rules of hyper-parameter in the algorithm, can assess the learning effect under different hyper-parameters, and can apply the rules to model training of machine learning so as to achieve the aim of automatically selecting the suitable hyper-parameter,and then the whole optimization process of hyper-parameter is automatically completed, so that the optimization efficiency is high and the usage complexity of the algorithm can be greatly reduced.

Description

technical field [0001] The present invention relates to the field of computer technology, in particular to a hyperparameter automatic optimization method and system for unsupervised machine learning. Background technique [0002] The parameters of machine learning algorithms mainly include hyperparameters and common parameters. Among them, hyperparameters are parameters whose values ​​are set before starting the learning process, rather than parameter data obtained through training. Hyperparameters define higher-level concepts about the model, such as complexity or learning ability, and these hyperparameters have a very large impact on algorithm performance. [0003] In the existing technology, the tuning of hyperparameters of unsupervised machine learning algorithms usually requires expert experience. For a given problem, it is generally impossible to know the optimal value of the hyperparameters of the machine learning model, and it is often necessary to use empirical rule...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06N99/00G06N3/08
CPCG06N3/088G06N20/00
Inventor 王建民龙明盛付博黄向东
Owner TSINGHUA UNIV
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