Support Vector Machine Base On Reduce White Noise Theory

Content: As one kind of new machine learning technology rose inthe early 1990s,support vector machine(SV M)has been extensivelystudied and has shown remarkable success in many applications. How-ever,the success of SV M is only limited in standard or ideal datadistribution status,when faced with non-ideal or exception data dis-tribution eases,the classical SV M performance dissatisfactory,andcan not meet the expected learning demands. Which in?uenced theSV Ms further extension and application in a great extent. In thelight of these problems of SV M,in this paper,we study the SV Malgorithms to cope with the non-ideal data distribution learning prob-lems,and give the suitable solutions.After reviewing the standard support vector machine and its math-ematical foundation. We introduce some conception about white noise,white Gaussian noise. Then,we study the classification algorithms ofSV M on the situation when training group is intervened with noise.This Problem has universal significance in practical applications,dueto the limit of subjective and objective condition,we can hardly en-sure that all the input information of training instances are clear orprecise,on the contrary,there are often mixed up with some un-certain or noisy information inside the training samples. By trans-forming the experience risk measurement of the traditional SupportVector Machine algorithm. We proposed gray information supportvector machine classification algorithm respectively,which uses theexperience risk measurement number to represent the uncertain infor-mation,transforms the noise information input to vector form,andextends traditional operation to new function. We also can adjustingthe parameters of kernel function. Using this that we also can con-trol or decrease the random noise. We give a new SV M model,in the model,if we know the probability of noise,we can construct thecontrol noise of function. At last,there is a concrete model that con-trol White Gaussian Noise is given.