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/291828/8392674
m svm.m
function [D, a_star] = SVM(train_features, train_targets, params, region)
% Classify using (a very simple implementation of) the support vector machine algorithm
%
% Inputs:
% features- Train f
www.eeworm.com/read/191902/8417104
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/191902/8417135
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/191902/8417169
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/191902/8417179
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/191902/8417284
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
www.eeworm.com/read/191902/8417390
m rce.m
function D = RCE(train_features, train_targets, lambda_m, region)
% Classify using the reduced coulomb energy algorithm
% Inputs:
% features - Train features
% targets - Train targets
% la
www.eeworm.com/read/191902/8417431
m stumps.m
function [D, w] = Stumps(train_features, train_targets, params, region)
% Classify using the least-squares algorithm
% Inputs:
% features- Train features
% targets - Train targets
% weights -
www.eeworm.com/read/286662/8751681
m deterministic_boltzmann.m
function [test_targets, updates] = Deterministic_Boltzmann(train_patterns, train_targets, test_patterns, params);
% Classify using the deterministic Boltzmann algorithm
% Inputs:
% train_pattern
www.eeworm.com/read/286662/8751738
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:
%