SVM增量式学习的自顺应与优化的MATLAB代码

[{"title":"（30个子文件152KB）SVM增量式学习的自适应与优化的MATLAB代码","children":[{"title":"Incremental-SVM-Learning-in-MATLAB-master","children":[{"title":"svmtrain2.m <span style='color:#111;'>9.88KB</span>","children":null,"spread":false},{"title":"looest.m <span style='color:#111;'>3.69KB</span>","children":null,"spread":false},{"title":"gpl.txt <span style='color:#111;'>11.83KB</span>","children":null,"spread":false},{"title":"gestloo.m <span style='color:#111;'>1.64KB</span>","children":null,"spread":false},{"title":"min_delta_p_c.m <span style='color:#111;'>3.55KB</span>","children":null,"spread":false},{"title":"kernel.m <span style='color:#111;'>835B</span>","children":null,"spread":false},{"title":"unlearn.m <span style='color:#111;'>5.87KB</span>","children":null,"spread":false},{"title":"min_delta_p_s.m <span style='color:#111;'>4.14KB</span>","children":null,"spread":false},{"title":"svmeval.m <span style='color:#111;'>2.49KB</span>","children":null,"spread":false},{"title":"min_delta_acb.m <span style='color:#111;'>4.38KB</span>","children":null,"spread":false},{"title":"SVMIncrementalLearningAdaptationandOptimization-DiehlandCauwenberghs-2003.pdf <span style='color:#111;'>130.28KB</span>","children":null,"spread":false},{"title":"perts.m <span style='color:#111;'>268B</span>","children":null,"spread":false},{"title":"min_delta.m <span style='color:#111;'>1.38KB</span>","children":null,"spread":false},{"title":"saveclass.m <span style='color:#111;'>1.34KB</span>","children":null,"spread":false},{"title":"updateRQ.m <span style='color:#111;'>2.09KB</span>","children":null,"spread":false},{"title":"bookkeeping.m <span style='color:#111;'>1.75KB</span>","children":null,"spread":false},{"title":"move_indr.m <span style='color:#111;'>1.41KB</span>","children":null,"spread":false},{"title":"nonlindata100.mat <span style='color:#111;'>1.82KB</span>","children":null,"spread":false},{"title":"move_ind.m <span style='color:#111;'>596B</span>","children":null,"spread":false},{"title":"kevals.m <span style='color:#111;'>271B</span>","children":null,"spread":false},{"title":"kevalsreset.m <span style='color:#111;'>216B</span>","children":null,"spread":false},{"title":"testresults.txt <span style='color:#111;'>759B</span>","children":null,"spread":false},{"title":"README <span style='color:#111;'>4.14KB</span>","children":null,"spread":false},{"title":"loadclass.m <span style='color:#111;'>1.43KB</span>","children":null,"spread":false},{"title":"perturbc.m <span style='color:#111;'>6.72KB</span>","children":null,"spread":false},{"title":"plot2dkm.m <span style='color:#111;'>1.79KB</span>","children":null,"spread":false},{"title":"svmloo.m <span style='color:#111;'>4.52KB</span>","children":null,"spread":false},{"title":"svmtrain.m <span style='color:#111;'>7.33KB</span>","children":null,"spread":false},{"title":"learn.m <span style='color:#111;'>4.73KB</span>","children":null,"spread":false},{"title":"perturbk.m <span style='color:#111;'>7.55KB</span>","children":null,"spread":false}],"spread":false}],"spread":true}]