代码搜索：Multi-class
找到约 534 项符合「Multi-class」的源代码
代码结果 534
www.eeworm.com/read/299459/7850437
m~ contents.m~
% Support Vector Machines.
%
% bsvm2 - Multi-class BSVM with L2-soft margin.
% evalsvm - Trains and evaluates Support Vector Machines classifier.
% mvsvmclass - Majority voting multi-cla
www.eeworm.com/read/398324/7994453
m train.m
function net = train(net, tutor, varargin)
% TRAIN
%
% Train a dag-svm multi-class support vector classifier network using the
% specified tutor to train each component two-class network.
%
www.eeworm.com/read/398324/7994461
m dagsvm.m
function net = dagsvm(arg)
% PAIRWISE
%
% Construct a dag-svm multi-class support vector classification network.
%
% Examples:
%
% % default constructor (a 0-class dagsvm network!)
%
%
www.eeworm.com/read/398324/7994616
m train.m
function net = train(net, tutor, varargin)
% TRAIN
%
% Train a dag-svm multi-class support vector classifier network using the
% specified tutor to train each component two-class network.
%
www.eeworm.com/read/398324/7994627
m dagsvm.m
function net = dagsvm(arg)
% PAIRWISE
%
% Construct a dag-svm multi-class support vector classification network.
%
% Examples:
%
% % default constructor (a 0-class dagsvm network!)
%
%
www.eeworm.com/read/128468/14295503
m contents.m
% Support Vector Machines.
%
% msmo - Multi-class version of SMO.
% msvmclass - Multi-class version of SVMCLASS.
% msvmmot - Multi-class version of SVMMOT.
% ka - Kernel-Adatron algo
www.eeworm.com/read/367442/9747937
m multisvmdemo1.m
% Demonstration of multi-class SVM learning.
% Statistical Pattern Recognition Toolbox, Vojtech Franc, Vaclav Hlavac
% (c) Czech Technical University Prague, http://cmp.felk.cvut.cz
% Modifications
%
www.eeworm.com/read/367442/9747996
m contents.m
% Support Vector Machines.
%
% msmo - Multi-class version of SMO.
% msvmclass - Multi-class version of SVMCLASS.
% msvmmot - Multi-class version of SVMMOT.
% ka - Kernel-Adatron algo
www.eeworm.com/read/367442/9748007
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/493294/6400452
m multic.m
%MULTIC Make a multi-class classifier
%
% W = MULTIC(A,V)
%
% Train the (untrained!) one-class classifier V on each of the classes
% in A, and combine it to a multi-class classifier W. If an object