On Issues And Applications For Support Vector Machine

As a tool of the structural risk minimization principle,support vector machine(SVM) brings along a bunch of advantages over other approaches,including uniqueness,globality,simple structure and good generalization properties.Because of its excellent learning power,SVM has become the topic of machine learning and successfully used over a lot of applications.In this dissertation,several aspects about SVM have been studied substantially as follows:(1) The idea in sparse algorithms for the least squares SVM(LS-SVM) is greedy,which causes the hyperplane to be not sparse enough.A novel Invfitting approach to analyze support vectors is shown,in which some support vector has smallest impact on the decision function will be deleted in iteration.Combine it and the Backfitting,a more global optimized HBILS-SVM algorithm is developed, which can effectively avoid the local solution,reduce the number of support vectors,and then derive a more sparse decision function.(2) The reduced convex hull(RCH) changes the shape of the convex hull of samples,and provides the necessary but not sufficient condition for the representation of its extreme points.The compressed convex hull(CCH) for avoiding these deficiencies is introduced.A CCH-based geometric SVM is shown after discussing the theory of CCH.Further,based on the characteristic of extreme points in CCH,a probabilistic accelerated CCH-based geometric SVM is presented to improve the computational speed.(3) A TM-ν-SVM is proposed to determine the regularization factor in TMSVM, in which the bounds of the fractions of the margin errors and sub-support vectors are both lower than those inν-SVM,i.e.,it derives better performance thanν-SVM.The geometric framework for TM-ν-SVM indicates that it is equivalent to finding the pair of closest points of the two soft compressed convex hulls (SCCH).Based on the discussion of the geometric propensities of SCCH,the corresponding geometric algorithm is developed. (4) An overview on the sample shift(SS) method in transforming support vector regression(SVR) to classification is given,a novel empirical gradient-based sample shift(GSS) approach is shown to solve the shortage in SS.Further,to reduce the impact of noise on empirical gradient,an online feature gradient-based sample shift(OFGSS) method by combining the geometric SVM is developed. Compared to the former,it reduces the impact of the shift value and noise,i.e., it has good generalization.(5) After comparing the merits and shortages of the incrcmcntal SVM with the gcometric SVM,a model selection way based on thc later is discussed.It combines the merits of the geometric SVM and the approximate gradient computation of the kernel parameters,which provides a rapid method to determine the kernel parameters in SVM.(6) After reviewing on a variety of algorithms for transductive support vector machine(TSVM),a sequential minimization algorithm for TSVM is introduced by introducing the sequential minimization idea to estimate Lagrange coefficients after adjusting the temporary label of a test sample,and then derives the new decision function and the estimation of empirical risk.This algorithm can solve the deficiency of the overly-simple estimation of empirical risk.(7) An application in predicting protein-protein interactions of SVM is discussed. In this application the information of the structure domain and sequcncc of proteins are respectively used to construct novel vector representation,which can effectively predict protein-protein interactions and interface sites.Numerical simulations show that the proposed vector representation derives bcttcr prediction performance than others.