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/372113/9521270
m balanced_winnow.m
function [test_targets, a_plus, a_minus] = Balanced_Winnow(train_patterns, train_targets, test_patterns, params)
% Classify using the balanced Winnow algorithm
% Inputs:
% training_patterns -
www.eeworm.com/read/372113/9521287
m store_grabbag.m
function test_targets = Store_Grabbag(train_patterns, train_targets, test_patterns, Knn)
% Classify using the store-grabbag algorithm (an improvement on the nearest neighbor)
% Inputs:
% train_p
www.eeworm.com/read/372113/9521331
m pnn.m
function test_targets = PNN(train_patterns, train_targets, test_patterns, sigma)
% Classify using a probabilistic neural network
% Inputs:
% train_patterns - Train patterns
% train_targets - Tr
www.eeworm.com/read/372113/9521354
m em.m
function [test_targets, param_struct] = EM(train_patterns, train_targets, test_patterns, Ngaussians)
% Classify using the expectation-maximization algorithm
% Inputs:
% train_patterns - Train pa
www.eeworm.com/read/372113/9521383
m pocket.m
function [test_targets, w_pocket] = Pocket(train_patterns, train_targets, test_patterns, alg_param)
% Classify using the pocket algorithm (an improvement on the perceptron)
% Inputs:
% train_pat
www.eeworm.com/read/372113/9521389
m gibbs.m
function test_targets = Gibbs(train_patterns, train_targets, test_patterns, Ndiv)
% Classify using the Gibbs algorithm
% Inputs:
% train_patterns - Train patterns
% train_targets - Train target
www.eeworm.com/read/372113/9521393
m stumps.m
function [test_targets, w] = Stumps(train_patterns, train_targets, test_patterns, params)
% Classify using simple stumps algorithm
% Inputs:
% train_patterns - Train patterns
% train_targets -
www.eeworm.com/read/362008/10023770
m parzen.m
function test_targets = parzen(train_patterns, train_targets, test_patterns, hn)
% Classify using the Parzen windows algorithm
% Inputs:
% train_patterns - Train patterns
% train_targets - Trai
www.eeworm.com/read/362008/10023791
m discrete_bayes.m
function test_targets = Discrete_Bayes(train_patterns, train_targets, test_patterns, cost)
% Classify discrete patterns using the Bayes decision theory
% Inputs:
% train_patterns - Train pattern
www.eeworm.com/read/362008/10023864
m projection_pursuit.m
function [test_targets, V, Wo] = Projection_Pursuit(train_patterns, train_targets, test_patterns, Ncomponents)
% Classify using projection pursuit regression
% Inputs:
% train_patterns - Train p