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/398324/7994541
m train.m
function net = train(tutor, x, y, C, kernel, zeta, net)
% TRAIN
%
% Train a support vector classification network, using the sequential minimal
% optimisation algorithm.
%
% net = train(tut
www.eeworm.com/read/397102/8068685
m testd.m
%TESTD Classification error estimate
%
% [e,j,k,l] = testd(A,W,r,iter)
%
% Test of dataset A on the classifier defined by W. Returns:
% e - the fraction of A that is incorrectly classified by W.
%
www.eeworm.com/read/397099/8068968
m multialgorithms_commands.m
function multialgorithms_commands(command)
%This function processes events from the multi-algorithm GUI screen
switch(command)
case 'Init'
Algorithms = read_algorithms('Classification.tx
www.eeworm.com/read/245941/12771050
m multialgorithms_commands.m
function multialgorithms_commands(command)
%This function processes events from the multi-algorithm GUI screen
switch(command)
case 'Init'
Algorithms = read_algorithms('Classification.tx
www.eeworm.com/read/245176/12813287
m train.m
function net = train(tutor, x, y, C, kernel, zeta, net)
% TRAIN
%
% Train a support vector classification network, using the sequential minimal
% optimisation algorithm.
%
% net = train(tut
www.eeworm.com/read/245176/12813338
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/143745/12847779
m svcinfo.m
function svcinfo(trn,tst,ker,alpha,bias)
%SVCINFO Support Vector Classification Results
%
% Usage: svcinfo(trn,tst,ker,alpha,bias)
%
% Parameters: trn - Training set
% tst - Test
www.eeworm.com/read/143706/12849962
m confmat.m
function [C,rate]=confmat(Y,T)
%CONFMAT Compute a confusion matrix.
%
% Description
% [C, RATE] = CONFMAT(Y, T) computes the confusion matrix C and
% classification performance RATE for the prediction
www.eeworm.com/read/143706/12849984
m demmlp2.m
%DEMMLP2 Demonstrate simple classification using a multi-layer perceptron
%
% Description
% The problem consists of input data in two dimensions drawn from a
% mixture of three Gaussians: two of which
www.eeworm.com/read/330850/12865047
m multialgorithms_commands.m
function multialgorithms_commands(command)
%This function processes events from the multi-algorithm GUI screen
switch(command)
case 'Init'
Algorithms = read_algorithms('Classification.tx