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\LS_SVMlab\deltablssvm.m
function model = deltablssvm(model,a1,a2) % Bias term correction for the LS-SVM classifier % % >> model = deltablssvm(model, b_new) % % This function is only useful in the object oriented function % interface. Set explicitly the bias term b_new of the LS-SVM model. % % Full syntax % % >> model = deltablssvm(model, b_new) % % Outputs % model : Object oriented representation of the LS-SVM model with initial hyperparameters % Inputs % model : Object oriented representation of the LS-SVM model % b_new : m x 1 vector with new bias term(s) for the model % % See also: % roc, trainlssvm, simlssvm, changelssvm % Copyright (c) 2002, KULeuven-ESAT-SCD, License & help @ http://www.esat.kuleuven.ac.be/sista/lssvmlab if iscell(model), model = initlssvm(model{:}); end if iscell(a1), model.alpha = a1{1}; model.b = a1{2}; model.status = 'trained'; deltab = a2; else deltab = a1; end if ~(model.type(1)=='c' & model.y_dim==1), error('only for binary classification tasks'); end % without retraining model.b = deltab;function model = deltablssvm(model,a1,a2) % Set the bias of the binary classification model % % When training the LS-SVM in a standard way, no prior information % is incorporated. There exists however techniques who can % calculate a bias term according to another criterium. This % function allows to set the corrected bias in the final LS-SVM model. % % model = deltablssvm(model,newbias) % model = deltablssvm({X,Y,'classification',gam,sig2},{alpha,b},newbias) % % see also: % roc, changelssvm % copyright if iscell(model), model = initlssvm(model{:}); end if iscell(a1), model.alpha = a1{1}; model.b = a1{2}; model.status = 'trained'; deltab = a2; else deltab = a1; end if ~(model.type(1)=='c' & model.y_dim==1), error('only for binary classification tasks'); end % without retraining model.b = deltab;