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/129915/14217771
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
www.eeworm.com/read/129915/14217779
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
www.eeworm.com/read/129915/14217795
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
www.eeworm.com/read/129915/14217796
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
www.eeworm.com/read/129915/14217797
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
www.eeworm.com/read/129915/14217798
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
www.eeworm.com/read/429426/1949351
extra entries.extra
D/Associate////
D/Classify////
D/Data////
D/Evaluate////
D/Other////
D/Visualize////
D/icons////
/OWOptions.py///1052316423/
/ColorPalette.py///1129189428/
/OWClusterOptimization.py///1144650
www.eeworm.com/read/386597/2570093
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:
%
www.eeworm.com/read/386597/2570224
m components_with_df.m
function [test_targets, errors] = Components_with_DF(train_patterns, train_targets, test_patterns, Ncomponents)
% Classify points using component classifiers with discriminant functions
% Inputs:
www.eeworm.com/read/474600/6813406
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:
%