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/131588/14136251
m relaxation_bm.m
function [D, a] = Relaxation_BM(train_features, train_targets, params, region)
% Classify using the batch relaxation with margin algorithm
% Inputs:
% features - Train features
% targets -
www.eeworm.com/read/131588/14136332
m rda.m
function D = RDA (train_features, train_targets, lamda, region)
% Classify using the Regularized descriminant analysis (Friedman shrinkage algorithm)
% Inputs:
% features - Train features
% tar
www.eeworm.com/read/131588/14136338
m ml.m
function D = ML(train_features, train_targets, AlgorithmParameters, region)
% Classify using the maximum-likelyhood algorithm
% Inputs:
% features - Train features
% targets - Train targets
%
www.eeworm.com/read/131588/14136340
m local_polynomial.m
function D = Local_Polynomial(features, targets, Nlp, region)
% Classify using the local polynomial fitting
% Inputs:
% features - Train features
% targets - Train targets
% Nlp - Number of t
www.eeworm.com/read/131588/14136344
m backpropagation_stochastic.m
function [D, Wh, Wo] = Backpropagation_Stochastic(train_features, train_targets, params, region)
% Classify using a backpropagation network with stochastic learning algorithm
% Inputs:
% feature
www.eeworm.com/read/131588/14136382
m cart.m
function D = CART(train_features, train_targets, params, region)
% Classify using classification and regression trees
% Inputs:
% features - Train features
% targets - Train targets
% para
www.eeworm.com/read/131588/14136412
m marginalization.m
function D = Marginalization(train_features, train_targets, missing, region)
% Classify data with missing features using the marginal distribution
% This file is strongly made for only two feature
www.eeworm.com/read/129915/14217595
m parzen.m
function D = parzen(train_features, train_targets, hn, region)
% Classify using the Parzen windows algorithm
% Inputs:
% features - Train features
% targets - Train targets
% hn - No
www.eeworm.com/read/129915/14217596
m ml_diag.m
function D = ML_diag(train_features, train_targets, AlgorithmParameters, region)
% Classify using the maximum likelyhood algorithm with diagonal covariance matrices
% Inputs:
% features - Train
www.eeworm.com/read/129915/14217607
m multivariate_splines.m
function D = Multivariate_Splines(train_features, train_targets, params, region)
% Classify using multivariate adaptive regression splines
% Inputs:
% features - Train features
% targets -