基于 ML loss 的 SVM 分类算法.

The loss function of SVM is able to guarantee the high confidence of classification results, but it is also an unbounded convex function which is greatly affected by noise. In order to improve the classification effect of SVM in noisy environment, this paper proposed ML loss combined with pinball and LS loss functions to reduce the sensitivity to noise, which was applied to SVM to obtain MLSVM model. The algorithm simplified the solution process according to the characteristics of LS loss function with structural risk minimization and equality constraints, then used pinball loss function to determine the classification hyperplanes according to the max quantile distance between classification samples and used Lagrange function and other methods to work out the objective function and classification hyperplanes of MLSVM. Experiments on datasets show that compared with hinge SVM and other models, MLSVM is capable of reducing the sensitivity to noise in data and improving the recognition performance of noise-containing data. [ABSTRACT FROM AUTHOR]