代码搜索：Classify

找到约 2,639 项符合「Classify」的源代码

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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
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www.eeworm.com/read/386597/2570144

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
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www.eeworm.com/read/386597/2570183

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 -
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www.eeworm.com/read/386597/2570194

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
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www.eeworm.com/read/386597/2570202

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
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www.eeworm.com/read/386597/2570211

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
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www.eeworm.com/read/386597/2570223

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
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www.eeworm.com/read/386597/2570226

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
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www.eeworm.com/read/386597/2570228

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 -
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www.eeworm.com/read/475585/6773569

am makefile.am

# TODO(luc) Add 'doc' to this list when ready SUBDIRS = ccstruct ccutil classify cutil dict pageseg image textord viewer wordrec ccmain training tessdata testing dlltest java EXTRA_DIST = eurotext.ti
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