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/399996/7816890
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/399996/7816956
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/399996/7817026
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/397106/8067534
m nearest_neighbor.m
function D = Nearest_Neighbor(train_features, train_targets, Knn, region)
% Classify using the Nearest neighbor algorithm
% Inputs:
% features - Train features
% targets - Train targets
% Knn - Num
www.eeworm.com/read/397106/8067645
m perceptron.m
function D = Perceptron(train_features, train_targets, alg_param, region)
% Classify using the Perceptron algorithm
% Inputs:
% features - Train features
% targets - Train targets
% alg_param -
www.eeworm.com/read/397106/8067778
m store_grabbag.m
function D = Store_Grabbag(train_features, train_targets, Knn, region)
% Classify using the store-grabbag algorithm (an improvement on the nearest neighbor)
% Inputs:
% features - Train features
% t
www.eeworm.com/read/397106/8067794
m pnn.m
function D = PNN(train_features, train_targets, sigma, region)
% Classify using a probabilistic neural network
% Inputs:
% features- Train features
% targets - Train targets
% sigma - Gaussian wid
www.eeworm.com/read/397106/8067857
m pocket.m
function [D, w_pocket] = Pocket(train_features, train_targets, alg_param, region)
% Classify using the pocket algorithm (an improvement on the perceptron)
% Inputs:
% features - Train features
% tar
www.eeworm.com/read/397099/8068763
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/397099/8068794
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:
%