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/405069/11472250
m backpropagation_stochastic.m
function [test_targets, Wh, Wo, J] = Backpropagation_Stochastic(train_patterns, train_targets, test_patterns, params)
% Classify using a backpropagation network with stochastic learning algorithm
www.eeworm.com/read/405069/11472269
m cart.m
function test_targets = CART(train_patterns, train_targets, test_patterns, params)
% Classify using classification and regression trees
% Inputs:
% training_patterns - Train patterns
% traini
www.eeworm.com/read/405069/11472297
m backpropagation_recurrent.m
function [test_targets, W, J] = Backpropagation_Recurrent(train_patterns, train_targets, test_patterns, params)
% Classify using a backpropagation recurrent network with a batch learning algorithm
www.eeworm.com/read/157703/11670775
m perceptron.m
function D = Perceptron(train_features, train_targets, alg_param, region)
% Classify using the Perceptron algorithm (Fixed increment single-sample perceptron)
% Inputs:
% features - Train featur
www.eeworm.com/read/339628/12216116
m svm.m
function [test_targets, a_star] = SVM(train_patterns, train_targets, test_patterns, params)
% Classify using (a very simple implementation of) the support vector machine algorithm
%
% Inputs:
%
www.eeworm.com/read/131588/14136185
m minimum_cost.m
function D = Minimum_Cost(train_features, train_targets, lambda, region)
% Classify using the minimum error criterion via histogram estimation of the densities
% Inputs:
% features- Train featur
www.eeworm.com/read/131588/14136223
m optimal_brain_surgeon.m
function [D, Wh, Wo] = Optimal_Brain_Surgeon(train_features, train_targets, params, region)
% Classify using a backpropagation network with a batch learning algorithm and remove excess units
% usi
www.eeworm.com/read/131588/14136253
m perceptron.m
function D = Perceptron(train_features, train_targets, alg_param, region)
% Classify using the Perceptron algorithm (Fixed increment single-sample perceptron)
% Inputs:
% features - Train featur
www.eeworm.com/read/131588/14136264
m projection_pursuit.m
function [D, V, Wo] = Projection_Pursuit(train_features, train_targets, Ncomponents, region)
% Classify using projection pursuit regression
% Inputs:
% features- Train features
% targets - Trai
www.eeworm.com/read/131588/14136355
m balanced_winnow.m
function [D, a_plus, a_minus] = Balanced_Winnow(train_features, train_targets, params, region)
% Classify using the balanced Winnow algorithm
% Inputs:
% features - Train features
% targets