代码搜索：classifier
找到约 4,824 项符合「classifier」的源代码
代码结果 4,824
www.eeworm.com/read/159921/10588135
m m2osmo.m
function [model]=m2osmo( data, labels, ker, arg, C, eps, tol)
% M2OSMO One-Against-All multiclass SVM classifier using M-2-O
% transform and SMO.
% [model]=m2osmo( data, labels, ker, arg, C, eps, to
www.eeworm.com/read/159921/10588141
m~ multisvmdemo1.m~
% Demonstration of multi-class SVM learning.
% loads data
data = load('multisvm1');
% setting SVM parameters
ker='rbf';
arg=1;
C=inf;
% learning SVM classifier
[model]=m2osmo( data.X, data.I, ker,
www.eeworm.com/read/159921/10588326
m m2osor.m
function [model]=m2osor( data, labels, ker, arg, C, eps)
% M2OSOR One-Against-All multiclass SVM classifier using M-2-O
% transform and SOR.
% [model]=m2osor( data, labels, ker, arg, C, eps)
%
% It
www.eeworm.com/read/421949/10675966
m osusvmdemo.m
% ------- OSU SVM CLASSIFIER TOOLBOX Demonstrations---
%
% 1) Demonstrations of using C-SVM Classifers.
% 2) Demonstrations of using u-SVM Classifiers
% 3) Demonstration
www.eeworm.com/read/421949/10676808
m m2osmo.m
function [model]=m2osmo( data, labels, ker, arg, C, eps, tol)
% M2OSMO One-Against-All multiclass SVM classifier using M-2-O
% transform and SMO.
% [model]=m2osmo( data, labels, ker, arg, C, eps, to
www.eeworm.com/read/421949/10676819
m~ multisvmdemo1.m~
% Demonstration of multi-class SVM learning.
% loads data
data = load('multisvm1');
% setting SVM parameters
ker='rbf';
arg=1;
C=inf;
% learning SVM classifier
[model]=m2osmo( data.X, data.I, ker,
www.eeworm.com/read/421949/10676997
m m2osor.m
function [model]=m2osor( data, labels, ker, arg, C, eps)
% M2OSOR One-Against-All multiclass SVM classifier using M-2-O
% transform and SOR.
% [model]=m2osor( data, labels, ker, arg, C, eps)
%
% It
www.eeworm.com/read/418695/10935192
m udc.m
%UDC Uncorrelated normal based quadratic Bayes classifier
%
% W = udc(A)
%
% Computation a quadratic classifier between the classes in the
% dataset A assuming normal densities with uncorrelated f
www.eeworm.com/read/418695/10935622
m traincc.m
%TRAINCC Train combining classifier if needed
%
% W = traincc(A,W,cclassf)
%
% The combining classifier cclassf is trained by dataset A*W if it needs
% training. W is typically a set of stacked or par
www.eeworm.com/read/466591/7029543
m cerror.m
function error=cerror(y1,y2,label)
% CERROR Computes classification error.
%
% Synopsis:
% error = cerror(y1,y2)
% error = cerror(y1,y2,label)
%
% Description:
% error = cerror(y1,y2) returns clas