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/177129/9468917
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/177129/9468920
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/177129/9468949
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/177129/9469022
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/361769/10036403
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
% param
www.eeworm.com/read/353969/10401030
txt 数据挖掘中cart算法实现.txt
CART
function D = CART(train_features, train_targets, params, region)
% Classify using classification and regression trees
% Inputs:
% features - Train features
% targets - Train targe
www.eeworm.com/read/349842/10796631
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/349842/10796635
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/349842/10796662
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 -
www.eeworm.com/read/349842/10796670
m deterministic_boltzmann.m
function D = Deterministic_Boltzmann(train_features, train_targets, params, region);
% Classify using the deterministic Boltzmann algorithm
% Inputs:
% features - Train features
% targets - Tra