Implementation method for data characteristic-oriented synthetic kernel support vector machine

Abstract

The invention relates to an implementation method for a data characteristic-oriented synthetic kernel support vector machine, which comprises the following steps: pre-processing data and extracting, selecting and grouping characteristics: switching the data from the data space to the characteristic space, wherein the selected characteristic reflects the characteristic of a classification recognition object; training and verifying a sample and selecting the sample to be classified; selecting and implementing a classifier: selecting the data characteristic-oriented synthetic kernel support vector machine classifier so as to obtain the synthetic kernel parameter through calculation; training the classifier: conducting learning training to DOCKSVM classifier by training the sample; verifying the performance of the classifier: checking performance of the samples under verification in classification so as to obtain the DOCKSVM classifier with satisfied verification; classifying the samples to be classified; and outputting the classification result. The method of the invention more objectively integrates the data biochemical characteristic of the application field into the support vector machine obtained by final training according to the data characteristic of the application field so as to improve the accuracy of the result of specific classification and recognition and achieve wide application.

Description

technical field [0001] The invention relates to the technical field of computer image processing and pattern recognition, in particular to a realization method of a synthetic kernel support vector machine oriented to data characteristics. Background technique [0002] The essence of support vector machine is a kernel-based method, so the selection and construction of kernel function has an important impact on the final performance of support vector machine. [0003] In recent years, more and more studies in related fields have shown that the kernel function has a certain impact on the classification accuracy, and it is of great research significance to choose, improve or modify the kernel function reasonably. Amari & Wu used the experimental data to modify the kernel function in 1999 to improve the performance of the SVM (support vector machine, support vector machine) classifier, so that it can better match the actual problem, so as to improve the accuracy of the SVM classi...

AI Technical Summary

Problems solved by technology

Each kernel in the synthetic kernel corresponds to different feature information or combinations of specific classification categories, so the weight coefficient of the kernel represents the degree of its influence on the classification result. The existing problem is how to reasonably construct the synthetic kernel with different characteristics and determine the corresponding reasonable weight of

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