gusucode.com > 支持向量机工具箱 - LIBSVM OSU_SVM LS_SVM源码程序 > 支持向量机工具箱 - LIBSVM OSU_SVM LS_SVM\stprtool\linear\anderson\andrerr.m
function [maxError,minr]=andrerr(MI,SIGMA,J,alpha,theta) % ANDRERR classification erorr, Generalized Anderson's task. % [maxError,minr]=andrerr(MI,SIGMA,J,alpha,theta) % % ANDERROR computes upper limit of probability of a wrong % classification in the Generalized Anderson's task (GAT) for % given solution. For more details on GAT refer to book SH10 % or functions GANDERS, GANDERS2, EANDERS. % % Input matrices MI, SIGMA and vector J describe input of the % GAT and pair alpha, theta is given solution of the GAT. % % Input: % MI [NxK] cointains N-dimensinal vectors of mean values for % K normal distributions so that MI=[Mi1,Mi2,...,MiK]. % SIGMA [Nx(N*K)] contains N-by-N covariance matrices again for % K normal distributions, SIGMA=[Sigma1,Sigma2,...,SigmaK]. % J [1xK] contains labels for each pair {Mi,Sigma}, for example % J(1) is label for first pair Mi1, Sigma1. % alpha [Nx1], theta [1x1] normal vector alpha and threshold theta % determine solution of the GAT. % % Output: % maxError [1x1] is upper limit of probability of bad classification. % minr [1x1] is parameter of solution of the GAT which determines % radius of the smallest ellipsoid. For more detail see SH10. % % Statistical Pattern Recognition Toolbox, Vojtech Franc, Vaclav Hlavac % (c) Czech Technical University Prague, http://cmp.felk.cvut.cz % Written Vojtech Franc (diploma thesis) 02.01.2000 % Modifications % 24. 6.00 V. Hlavac, comments polished. % get dimension N and number of distributions N=size(MI,1); K=size(MI,2); % [alpha,MI,SG]=ctransf(alpha,theta,MI,J,SIGMA); % find distances between MIs and contact points minr=min((alpha'*MI)./sqrt( reshape(alpha'*SG,N+1,K)'*alpha )'); % computes maximal error maxError=1-cdf('norm',minr,0,1);