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Data processing method and data processing device

A data processing and parameter technology, applied in the field of information processing, can solve problems such as limiting the universality of methods

Inactive Publication Date: 2020-05-19
恩亿科(北京)数据科技有限公司
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Problems solved by technology

The Bayesian optimization algorithm needs to assume that the optimization function of the model obeys the Gaussian distribution, which greatly limits the universality of the method

Method used

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  • Data processing method and data processing device
  • Data processing method and data processing device
  • Data processing method and data processing device

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Embodiment Construction

[0048] In order to make the purpose, technical solutions and advantages of the embodiments of the present application clearer, the embodiments of the embodiments of the present application will be described in detail below with reference to the accompanying drawings. It should be noted that, in the case of no conflict, the embodiments in the embodiments of the present application and the features in the embodiments can be combined arbitrarily with each other.

[0049]In order to automatically adjust model parameters efficiently, accurately and with universality, the present application proposes a general algorithm for automatic parameter adjustment of classification algorithm models. The algorithm can automatically adjust the parameters of the algorithm model for different data, even the training data containing noise. In addition, if the model cannot be judged by only one objective optimization function, and multi-objective optimization is required, the algorithm can also be ...

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Abstract

The embodiment of the invention discloses a data processing method and a data processing device. The method comprises the following steps: obtaining initialized populations corresponding to m parameters needing to be adjusted, wherein the initialized populations comprise lambda individuals, each individual comprises initial values corresponding to the m parameters, and m is a positive integer; performing iterative processing on the initialized populations to obtain lambda optimal individuals; calculating the mutation probability of the optimal individuals; executing mutation operation on at least one parameter in at least one optimal individual according to the mutation probability to obtain a new population generated by the mutation operation; and selecting an individual meeting a presetoptimal selection strategy from the new population generated by the mutation operation, and determining parameter values corresponding to the m parameters.

Description

technical field [0001] The embodiments of the present application relate to the field of information processing, in particular to a data processing method and device. Background technique [0002] Machine learning algorithms often model the collected sample data in order to solve a certain problem and discover the laws in the data. The problems that need to be solved often do not have exact solutions, and generally need to be turned into optimization problems to continuously approach the optimal solution. The performance of the model is often closely related to the parameters of the model, that is, whether it can better solve the proposed problem needs to adjust the parameters of the model efficiently and accurately. Among them, parameters are algorithm parameters in machine learning, which are generally divided into model parameters and model hyperparameters, which are the key to the algorithm. Model parameters are learned based on data and do not need to be manually set,...

Claims

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Application Information

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IPC IPC(8): G06N3/00
CPCG06N3/006
Inventor 范慧婷卢亿雷
Owner 恩亿科(北京)数据科技有限公司
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