Construction method and device, classification method and device of support vector machine

A support vector machine and construction method technology, applied in the direction of instruments, computer components, special data processing applications, etc., can solve data disasters, high computational complexity and other problems

Inactive Publication Date: 2014-02-26
CHINA UNIV OF PETROLEUM (BEIJING)
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Problems solved by technology

The usual L2 norm regularized SVM needs to traverse all combinations of feature dimensions to find

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  • Construction method and device, classification method and device of support vector machine
  • Construction method and device, classification method and device of support vector machine
  • Construction method and device, classification method and device of support vector machine

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[0105] In order to make the objectives, technical solutions, and advantages of the present invention clearer, the present invention will be further described in detail below in conjunction with the embodiments and the drawings. Here, the exemplary embodiments of the present invention and the description thereof are used to explain the present invention, but not as a limitation to the present invention.

[0106] The inventors analyzed and studied the experimental source code of the L2 norm-SVM, L1 norm-SVM and L0 norm-SVM classification algorithms and found that the L2 norm, L1 norm and L0 norm regularized SVM classification algorithm training The obtained model weight vector w is not a sparse vector, that is, the magnitude of each component of the model weight vector w is basically the same. Feature selection requires artificially retaining the d components with the largest value in the model weight vector w, and zeroing the remaining components in the model weight vector w, and ...

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Abstract

The invention provides a construction method and device, a classification method and device of support vector machine. The construction method and device, the classification method and device of support vector machine comprises: make sure non-linear weighted kernel function of a single variable; make sure nonconvex Lpfraction norm penalty object function on the base of the weighted kernel function of a single variable; make use of nonconvex Lpfraction norm penalty object function to construct support vector machine. Compared with the technical proposal which needs to traverse all characteristic combination of dimension to look for the desired characteristics when high-dimensional data of small sample is classified in the existing technology, the invention constructs the support vector machine and the support vector machine is used to classify the high-dimensional data of small sample so as to produce more sparse model, to achieve feature selection of any structure more accurately, to obtain better prediction accuracy, to reduce computation complexity largely and to avoid data disaster.

Description

technical field [0001] The present invention relates to the technical field of intelligent information processing, in particular to a construction method and device of a Support Vector Machine (SVM for short) classifier, and a classification method and device. technical background [0002] In the fields of computer vision, such as three-dimensional brain magnetic resonance imaging, bioinformatics, cancer microarray gene diagnosis, and customer relationship analysis on commercial websites, there are a large number of high-dimensional small-sample data, which is characterized by high-dimensional and small-sample data. Dimensional data, the sample dimension is as high as several thousand to tens of thousands of dimensions. It is difficult to obtain the class labels of high-dimensional small-sample data samples. If manual labeling is used, the cost is relatively high, resulting in fewer samples with class labels, and the number of sample-class label pairs is also relatively smal...

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Application Information

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IPC IPC(8): G06F17/30G06K9/62
CPCG06F18/24
Inventor 刘建伟刘媛罗雄麟
Owner CHINA UNIV OF PETROLEUM (BEIJING)
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