Model parameter optimizing method and device

A technology of model parameters and models, applied in the field of information, can solve problems such as the inability to obtain the penalty coefficient C and the kernel width γ, local optimization, affecting the model classification and prediction effect, etc.

Inactive Publication Date: 2017-05-10
WENZHOU UNIVERSITY
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Problems solved by technology

The existing technology mainly uses the grid search method to determine the penalty coefficient C and the kernel width γ. However, the grid search method is easy to fall int

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  • Model parameter optimizing method and device
  • Model parameter optimizing method and device

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[0063] In order to make the object, technical solution and advantages of the present invention clearer, the present invention will be further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described here are only used to explain the present invention, not to limit the present invention.

[0064] In the embodiment of the present invention, the moth optimization algorithm of chaos theory is fused with the kernel limit learning machine; by standardizing the acquired sample data; and then using the standardized sample data, the kernel limit is obtained by combining the moth optimization algorithm of chaos theory The optimal penalty coefficient C and the optimal kernel width γ of the learning machine; finally, according to the standardized sample data, the optimal penalty coefficient C and the optimal kernel width γ, a target classification prediction model is constructed; The problem th...

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Abstract

The invention is suitable for a field of information technology and provides a model parameter optimizing method and device. The method includes obtaining sample data and performing standardizing treatment on the sample data; obtaining the optimal penalty coefficient C and the optimal kernel scale Gamma of a kernel extreme learning machine through combining with a Moth optimization algorithm of chaos theory by adopting the sample data subjected to standardization; according to the sample data subjected to standardization, the optimal penalty coefficient C and the optimal kernel scale Gamma, constructing a target classification predication model. The invention solves a problem that the optimal penalty coefficient C and the optimal kernel scale Gamma cannot be obtained by utilizing a grid searching method in the prior art and is beneficial to improvement of effect of the constructed model in classification and predication of determined problems.

Description

technical field [0001] The invention belongs to the field of information technology, and in particular relates to a method and device for optimizing model parameters. Background technique [0002] The traditional neural network method has the disadvantage of being easily trapped in a local minimum because of the gradient descent method for training and learning. At the same time, it is difficult to establish an optimal model because a large number of parameters need to be adjusted during the network construction process. In order to overcome the above shortcomings of neural networks, Professor Huang Guangbin from Nanyang Technological University and others proposed a new neural network learning method, namely extreme learning machine. However, since the input parameters of the extreme learning machine are randomly generated, the performance of the model will be unstable; in this regard, Professor Huang Guangbin and others continued to propose the kernel extreme learning mach...

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Application Information

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IPC IPC(8): G06N3/08
CPCG06N3/086
Inventor 陈慧灵王名镜赵学华朱彬磊王科杰柳建飞童长飞蔡振闹
Owner WENZHOU UNIVERSITY
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