代码搜索：classification
找到约 3,679 项符合「classification」的源代码
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www.eeworm.com/read/289680/8535156
m fwd.m
function y = fwd(net,x)
% FWD
%
% Compute the output of a multi-class support vector classification network.
%
% y = fwd(net, x);
%
% where x is a matrix of input patterns, where each colu
www.eeworm.com/read/289680/8535164
m fwd.m
function y = fwd(net, x)
% FWD
%
% Compute the output of a dag-svm multi-class support vector classification
% network.
%
% y = fwd(net, x);
%
% where x is a matrix of input patterns, in
www.eeworm.com/read/188280/8552159
m fwd.m
function y = fwd(net,x)
% FWD
%
% Compute the output of a multi-class support vector classification network.
%
% y = fwd(net, x);
%
% where x is a matrix of input patterns, where each colu
www.eeworm.com/read/188280/8552304
m fwd.m
function y = fwd(net,x)
% FWD
%
% Compute the output of a multi-class support vector classification network.
%
% y = fwd(net, x);
%
% where x is a matrix of input patterns, where each colu
www.eeworm.com/read/188280/8552314
m fwd.m
function y = fwd(net, x)
% FWD
%
% Compute the output of a dag-svm multi-class support vector classification
% network.
%
% y = fwd(net, x);
%
% where x is a matrix of input patterns, in
www.eeworm.com/read/388611/8597143
readme
Part I: Model Selection Tools
Introduction
===============
grid.py is a model selection tool for C-SVM classification using RBF
(radial basis function) kernel. It uses cross validation (CV) techni
www.eeworm.com/read/429504/8804807
m trainlssvm.m
function [model,b,X,Y] = trainlssvm(model,X,Y)
% Train the support values and the bias term of an LS-SVM for classification or function approximation
%
% >> [alpha, b] = trainlssvm({X,Y,type,gam,ke
www.eeworm.com/read/428849/8834402
pl references.pl
{
"Anderson62" =>"T.W.Anderson and R.R.Bahadur. Classification into two
multivariate normal distributions with differrentia covariance matrices.
Anals of Mathematical Statistics, 33:420--431, Ju
www.eeworm.com/read/428849/8834423
m contents.m
% Data sets used by the STPRtool.
%
% andersons_task - (dir) Input for demo on Generalized Anderson's task.
% binary_separable - (dir) Input for demo on Linear classification.
% gmm_sample - (
www.eeworm.com/read/428451/8867232
m trainlssvm.m
function [model,b,X,Y] = trainlssvm(model,X,Y)
% Train the support values and the bias term of an LS-SVM for classification or function approximation
%
% >> [alpha, b] = trainlssvm({X,Y,type,gam,ke