代码搜索：Classify

找到约 2,639 项符合「Classify」的源代码

代码结果 2,639
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www.eeworm.com/read/410924/11264860

m ada_boost.m

function D = ada_boost(train_features, train_targets, params, region); % Classify using the AdaBoost algorithm % Inputs: % features - Train features % targets - Train targets % Params - [Numbe
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www.eeworm.com/read/410924/11264962

m components_without_df.m

function D = Components_without_DF(train_features, train_targets, Classifiers, region) % Classify points using component classifiers with discriminant functions % Inputs: % train_features - Trai
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www.eeworm.com/read/410924/11265012

m store_grabbag.m

function D = Store_Grabbag(train_features, train_targets, Knn, region) % Classify using the store-grabbag algorithm (an improvement on the nearest neighbor) % Inputs: % features - Train features
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www.eeworm.com/read/410924/11265027

m pnn.m

function D = PNN(train_features, train_targets, sigma, region) % Classify using a probabilistic neural network % Inputs: % features- Train features % targets - Train targets % sigma - Gaussi
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www.eeworm.com/read/410924/11265045

m genetic_algorithm.m

function D = Genetic_Algorithm(train_features, train_targets, params, region); % Classify using a basic genetic algorithm % Inputs: % features - Train features % targets - Train targets % Para
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www.eeworm.com/read/410924/11265069

m pocket.m

function [D, w_pocket] = Pocket(train_features, train_targets, alg_param, region) % Classify using the pocket algorithm (an improvement on the perceptron) % Inputs: % features - Train features
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www.eeworm.com/read/410924/11265071

m components_with_df.m

function D = Components_with_DF(train_features, train_targets, Ncomponents, region) % Classify points using component classifiers with discriminant functions % Inputs: % train_features - Train f
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www.eeworm.com/read/410924/11265074

m relaxation_ssm.m

function [D, a] = Relaxation_SSM(train_features, train_targets, params, region) % Classify using the single-sample relaxation with margin algorithm % Inputs: % features - Train features % targe
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www.eeworm.com/read/410924/11265076

m gibbs.m

function D = Gibbs(train_features, train_targets, Ndiv, region) % Classify using the Gibbs algorithm % Inputs: % features- Train features % targets - Train targets % Ndiv - Resolution of th
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www.eeworm.com/read/405069/11472158

m ml_diag.m

function test_targets = ML_diag(train_patterns, train_targets, test_patterns, AlgorithmParameters) % Classify using the maximum likelyhood algorithm with diagonal covariance matrices % Inputs: %
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