Hybrid Fourier kernel function support vector machine text classification method

Abstract

The invention provides a hybrid Fourier kernel function support vector machine text classification method. According to different learning and generalization capacity of various kernel functions in asupport vector machine, a new hybrid Fourier kernel function is formed by linear weighted hybrid polynomial and Fourier kernel functions. As the learning and generalization capacity of the kernel functions greatly affects support vector machine classification effects, the polynomial kernel functions and the Fourier kernel functions are combined. High learning capacity of the Fourier kernel functions and the generalization capacity of the polynomial kernel functions are inherited, and the performance of a support vector machine classifier is improved. Moreover, compared with polynomial kernel functions, Gaussian kernel functions and Fourier kernel functions in single kernels and polynomial and Gaussian kernel combination kernel functions in hybrid kernel functions, the hybrid Fourier kernelfunctions have better generalization and learning capacity, and text classification effects are the best.

Description

technical field [0001] The invention is mainly applied to the natural language processing in machine learning, and in particular relates to a text classification method of a mixed Fourier kernel function support vector machine. Background technique [0002] With the advent of the era of big data, natural language processing, image processing and other related data processing have developed rapidly. Due to the high-dimensional features of text information, how to find unique rules in these complex high-dimensional features so as to serve people better in the future is an important research direction of statistical learning theory. Support Vector Machines (SupportVectorMachines, SVM) is a machine learning method based on statistical learning theory proposed by Vapnik et al. in 1995. SVM solves the non-linear problem by relying on various kernel functions. [0003] At present, SVM has also been widely studied in nonlinear text classification problems. The article [Liu Gaohui...

AI Technical Summary

Problems solved by technology

The article [J.A.K.Suykens, J.Vandewalle, Least squares support vectormachine classifiers, Neural Processing Letters 9(3), 293(1999).] proposed the least squares support vector machine to solve nonlinear problems, but the accuracy is not very high

Literature [Zhang Yong. Performance analysis of Fourier kernel in support vector machine [D]. East China Normal University. 2008.] studied N-dimensional Fourier kernel on the basis of one-dimensional Fourier kernel, but the experimental analysis showed that On the text classification problem, the classification effect of N-dimensional and one-dimensional Fourier kernel functions is similar

Images