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/474600/6813523
m backpropagation_cgd.m
function [test_targets, Wh, Wo, errors] = Backpropagation_CGD(train_patterns, train_targets, test_patterns, params)
% Classify using a backpropagation network with a batch learning algorithm and co
www.eeworm.com/read/474600/6813542
m rocchiobagging.m
function [test_targets] = RocchioBagging(train_patterns, train_targets, test_patterns, params)
% Classify using the Bagging algorithm
% Inputs:
% train_patterns - Train patterns
% train_targets
www.eeworm.com/read/474600/6813545
m backpropagation_sm.m
function [test_targets, Wh, Wo, J] = Backpropagation_SM(train_patterns, train_targets, test_patterns, params)
% Classify using a backpropagation network with stochastic learning algorithm with mome
www.eeworm.com/read/474600/6813585
m perceptron_vim.m
function [test_targets, a] = Perceptron_VIM(train_patterns, train_targets, test_patterns, params)
% Classify using the variable incerement Perceptron with margin algorithm
% Inputs:
% train_pat
www.eeworm.com/read/294005/8258141
cpp l7-parse-patterns.cpp
/*
Functions and classes which keep track of and use regexes to classify streams
of application data.
By Ethan Sommer and Matthew Strait
, (C) 200
www.eeworm.com/read/415311/11077000
m ls.m
function [D, w] = LS(train_features, train_targets, weights, region)
% Classify using the least-squares algorithm
% Inputs:
% features- Train features
% targets - Train targets
% Weights - Wei
www.eeworm.com/read/415311/11077031
m discrete_bayes.m
function D = Discrete_Bayes(train_features, train_targets, cost, region, test_feature)
% Classify discrete features using the Bayes decision theory
% Inputs:
% features - Train features
% targ
www.eeworm.com/read/415311/11077279
m rbf_network.m
function [D, mu, Wo] = RBF_Network(train_features, train_targets, Nh, region)
% Classify using a backpropagation network with a batch learning algorithm
% Inputs:
% features- Train features
% t
www.eeworm.com/read/410924/11264746
m ls.m
function [D, w] = LS(train_features, train_targets, weights, region)
% Classify using the least-squares algorithm
% Inputs:
% features- Train features
% targets - Train targets
% Weights - Wei
www.eeworm.com/read/191902/8417335
m ho_kashyap.m
function [D, w_percept, b] = Ho_Kashyap(train_features, train_targets, params, region)
% Classify using the using the Ho-Kashyap algorithm
% Inputs:
% features - Train features
% targets -