代码搜索：Classify
找到约 2,639 项符合「Classify」的源代码
代码结果 2,639
www.eeworm.com/read/286662/8751911
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
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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
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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/430096/8766351
java compareints.java
// control/CompareInts.java
// TIJ4 Chapter Control, Exercise 2, page 139
/* Write a program that generates 25 random int values. For each value, use an
* if-else statement to classify it as greate
www.eeworm.com/read/281848/9129755
java compareints.java
// control/CompareInts.java
// TIJ4 Chapter Control, Exercise 2, page 139
/* Write a program that generates 25 random int values. For each value, use an
* if-else statement to classify it as greate
www.eeworm.com/read/177129/9468955
m ho_kashyap.m
function [D, w_percept, b] = Ho_Kashyap(train_features, train_targets, params, region)
% Classify using the using the Ho-Kashyap algorithm
% Inputs:
% features - Train features
% targets -
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m ml_ii.m
function D = ML_II(train_features, train_targets, Ngaussians, region)
% Classify using the ML-II algorithm. This function accepts as inputs the maximum number
% of Gaussians per class and returns
www.eeworm.com/read/372113/9521091
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/372113/9521099
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/372113/9521117
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