SVM (support vector machine) parameter selection method based on multi-kernel function adaptive fusion

A support vector machine and parameter selection technology, which is applied to computer parts, instruments, character and pattern recognition, etc., can solve the problem of ideal classification effect of support vector machine

Inactive Publication Date: 2017-06-20
QUZHOU UNIV
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Problems solved by technology

However, based on the selection of weighting coefficients and kernel parameters based on these methods, the classification effect of support vector machine is not very ideal.

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  • SVM (support vector machine) parameter selection method based on multi-kernel function adaptive fusion
  • SVM (support vector machine) parameter selection method based on multi-kernel function adaptive fusion
  • SVM (support vector machine) parameter selection method based on multi-kernel function adaptive fusion

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Embodiment Construction

[0036] The present invention will be further described below in conjunction with accompanying drawing:

[0037] Problem Description:

[0038] The structure diagram of the support vector regression machine is as follows: figure 1 As shown, the key of support vector machine is the introduction of kernel function. The kernel function cleverly solves the problem of dimensionality disaster caused by mapping low-dimensional vectors to high-dimensional ones, and improves the nonlinear processing ability of machine learning. Each kernel function has its own characteristics, and support vector regression machines based on different kernel functions have different generalization capabilities. Kernel function is one of the most important problems in support vector machine, how to choose the appropriate kernel function is also a difficult problem without theoretical basis in support vector machine.

[0039] Let K(x i ,x j ) represents the kernel function, where x i , x j for the sa...

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Abstract

The invention discloses an SVM (support vector machine) parameter selection method based on multi-kernel function adaptive fusion. Compared with the prior art, the method achieves the specific analysis of the characteristics of a local kernel function, a global kernel function, a mixed kernel function and a multi-kernel function. The method comprises the steps: combining all fusion coefficients, kernel function parameters and regression parameters of the multi-kernel function together to serve as a parameter state vector, thereby enabling a model selection problem to be converted into a state estimation problem of a nonlinear system; carrying out the parameter estimation through fifth-order volume Kalman filtering, and achieving the adaptive fusion of weighted coefficients of the multi-kernel function and the selection of kernel parameters and regression parameters.

Description

technical field [0001] The invention relates to the field of data processing algorithms, in particular to a support vector machine parameter selection method based on multi-kernel function adaptive fusion. Background technique [0002] Support Vector Machine (Support Vector Machine, SVM) has attracted the attention and research of many scholars at home and abroad since it was first proposed by Corinna Cortes and Vapnik in 1995. Principles of Universal Learning Approach [1] . SVM has strong nonlinear processing ability and generalization ability, especially in solving nonlinear, small sample and high-dimensional pattern recognition, and has been widely used in the processing of classification and regression problems, such as image recognition, text Classification, face recognition and intrusion detection and other fields [2，3，4，5] . [0003] The core of SVM is the introduction of kernel functions. Each kernel function has its own characteristics and is highly targeted for...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06K9/62
CPCG06F18/2411G06F18/25
Inventor 王海伦蔡志宏叶虹王天真
Owner QUZHOU UNIV
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