Construction method and device as well as sorting method and device for support vector machine sorter

Abstract

The invention provides a construction method and a construction device as well as a sorting method and a sorting device for a support vector machine sorter, wherein the method comprises the steps of determining a non-linear weighted kernel function; determining a non-convex Lp fraction norm punishment target function based on the weighted kernel function; constructing the support vector machine sorter by utilizing the non-convex Lp fraction norm punishment target function. Compared with the technical scheme in the prior art, in which when high-dimensional small sample data are sorted, all characteristic-dimensional combinations need to be traversed to find the needed characteristics, the method has the advantage that the constructed support vector machine sorter can realize the characteristic selecting function of a sample original space after the non-linear kernel mapping; the method can be used for sorting the high-dimensional data to generate a more sparse model, realize more accurate characteristic selecting and obtain a better predication accuracy, so the calculation complexity is greatly reduced, and a data disaster is avoided.

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 (Support Vector Machine, SVM) classifier, and a classification method and device. Background technique [0002] In the fields of computer vision such as three-dimensional brain magnetic resonance imaging, bioinformatics, cancer microarray gene diagnosis, and commercial website customer relationship analysis, there are a large number of high-dimensional small sample data. The characteristic of high-dimensional small-sample data is that the samples are high-dimensional data, and it is difficult to obtain the class labels of the samples. If manual labeling is used, the cost will be high. Based on the above reasons, there are relatively few samples with class labels. However, the goal of classification prediction not only requires the algorithm to have accurate prediction performance, but ...

AI Technical Summary

Problems solved by technology

If this L1, L0 norm SVM classifier is used to classify high-dimensional small sample data, the amount of calculation is too large, and the current L1, L0, L2 norm regularized SVM classifiers usually cannot realize the original space characteristics of the sample. selection, feature selection function classifier design in the original space after non-linear kernel mapping cannot be achieved

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