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/191902/8417076
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/191902/8417086
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
www.eeworm.com/read/191902/8417100
m backpropagation_quickprop.m
function [D, Wh, Wo] = Backpropagation_Quickprop(train_features, train_targets, params, region)
% Classify using a backpropagation network with a batch learning algorithm and quickprop
% Inputs:
www.eeworm.com/read/191902/8417167
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/191902/8417249
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/191902/8417254
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/191902/8417257
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/191902/8417262
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/191902/8417331
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/191902/8417394
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