代码搜索：Classify
找到约 2,639 项符合「Classify」的源代码
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www.eeworm.com/read/386597/2570182
m locboost.m
function [test_targets, P, theta, phi] = LocBoost(train_patterns, train_targets, test_patterns, params)
% Classify using the local boosting algorithm
% Inputs:
% train_patterns - Train patterns
www.eeworm.com/read/386597/2570192
m lms.m
function [test_targets, a, updates] = LMS(train_patterns, train_targets, test_patterns, params)
% Classify using the least means square algorithm
% Inputs:
% train_patterns - Train patterns
% t
www.eeworm.com/read/386597/2570209
m genetic_algorithm.m
function test_targets = Genetic_Algorithm(train_patterns, train_targets, test_patterns, params)
% Classify using a basic genetic algorithm
% Inputs:
% training_patterns - Train patterns
% tra
www.eeworm.com/read/386597/2570225
m relaxation_ssm.m
function [test_targets, a] = Relaxation_SSM(train_patterns, train_targets, test_patterns, params)
% Classify using the single-sample relaxation with margin algorithm
% Inputs:
% train_patterns -
www.eeworm.com/read/369339/2800935
1 file.1
.th FILE I 1/16/75
.sh NAME
file \*- determine file type
.sh SYNOPSIS
.bd file
file ...
.sh DESCRIPTION
.it File
performs a series of tests on each argument
in an attempt to classify it.
If an argumen
www.eeworm.com/read/474600/6813409
m cascade_correlation.m
function [test_targets, Wh, Wo, J] = Cascade_Correlation(train_patterns, train_targets, test_patterns, params)
% Classify using a backpropagation network with the cascade-correlation algorithm
% I
www.eeworm.com/read/474600/6813415
m multivariate_splines.m
function test_targets = Multivariate_Splines(train_patterns, train_targets, test_patterns, params)
% Classify using multivariate adaptive regression splines
% Inputs:
% train_patterns - Train pa
www.eeworm.com/read/474600/6813431
m perceptron_batch.m
function [test_targets, a, updates] = Perceptron_Batch(train_patterns, train_targets, test_patterns, params)
% Classify using the batch Perceptron algorithm
% Inputs:
% train_patterns - Train pa
www.eeworm.com/read/474600/6813443
m optimal_brain_surgeon.m
function [test_targets, Wh, Wo, J] = Optimal_Brain_Surgeon(train_patterns, train_targets, test_patterns, params)
% Classify using a backpropagation network with a batch learning algorithm and remov
www.eeworm.com/read/474600/6813455
m relaxation_bm.m
function [test_targets, a] = Relaxation_BM(train_patterns, train_targets, test_patterns, params)
% Classify using the batch relaxation with margin algorithm
% Inputs:
% train_patterns - Train pa