www.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\svm\kernelskf.m
% KERNELSKF kernel Schlesinfer-Kozinec's algorithm. % [Alpha,bias,sol]=kernelskf(data,labels,stop,ker,arg,tmax,C) % [Alpha,bias,sol,t,kercnt,margin,trnerr]=kernelskf(...) % % KERNELSKF kernel Schlesinger-Kozinec's algorithm solves the % Support vector Machines problem with quadratic cost function % for classification violations. % % Inputs: % data [dim x N] training patterns % labels [1 x N] labels of training patterns % stop [1 x 2] if stop(1) == 1 then stopping condition m*-m < stop(2) % is used else stopping condition (m*-m)/m < stop(2) is used. % Where m* is the optimial margin and m is the margin of found % hyperplane (in the given feature space). % ker [string] kernel, see 'help kernel'. % arg [...] argument of given kernel, see 'help kernel'. % tmax [int] maximal number of iterations. % C [real] trade-off between margin and training error. % % Outputs: % Alpha [1xN] Lagrangians defining found decision rule. % bias [real] bias (threshold) of found decision rule. % sol [int] 1 solution is found % 0 algorithm stoped (t == tmax) before converged. % -1 hyperplane with margin greater then epsilon % does not exist. % t [int] number of iterations. % kercnt [int] number of kernel evaluations. % margin [real] margin between classes. % trnerr [real] training error. % % See also SVM. % % Statistical Pattern Recognition Toolbox, Vojtech Franc, Vaclav Hlavac % (c) Czech Technical University Prague, http://cmp.felk.cvut.cz % Written Vojtech Franc (diploma thesis) 02.11.1999, 13.4.2000 % Modifications % 19-Nov-2001, V.Franc %