代码搜索：classification
找到约 3,679 项符合「classification」的源代码
代码结果 3,679
www.eeworm.com/read/280595/10311517
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/161855/10361126
1 bayesol.1
\" t
.TH BAYESOL 1 "Bayesian Classification Tools" "Version 1.3" ""
.SH NAME
bayesol \- a Bayes solution calculator for use with dbacl.
.SH SYNOPSIS
.HP
.B bayesol
[-DVniv] -c
.I riskspec
[FILE]...
.
www.eeworm.com/read/161165/10442848
m svc_fun.m
function outPutPar=SVC_TwoClass(class_one,class_two,sizeTrainClassOne,sizeTrainClassTwo,kPar,C,kType)
% the function for generating the two_class based classification
% InputPar:
% class_one: the f
www.eeworm.com/read/160933/10469222
m svcm_run.m
function [ypred,margin] = svcm_run(xrun,xtrain,ytrain,atrain,btrain);
% function [ypred,margin] = svcm_run(xrun,xtrain,ytrain,atrain,btrain);
%
% support vector classification machine
% soft margin
%
www.eeworm.com/read/278889/10490548
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/351797/10609695
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/351797/10609862
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/351797/10609876
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/421949/10676080
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/273047/10930340
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