代码搜索：Classify
找到约 2,639 项符合「Classify」的源代码
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www.eeworm.com/read/191902/8417435
m ml_ii.m
function D = ML_II(train_features, train_targets, Ngaussians, region)
% Classify using the ML-II algorithm. This function accepts as inputs the maximum number
% of Gaussians per class and returns
www.eeworm.com/read/389442/8519786
m demop1.m
%% Classification with a 2-input Perceptron
% A 2-input hard limit neuron is trained to classify 5 input vectors into two
% categories.
%
% Copyright 1992-2002 The MathWorks, Inc.
% $Revision: 1.
www.eeworm.com/read/388092/8636300
m demop1.m
%% Classification with a 2-input Perceptron
% A 2-input hard limit neuron is trained to classify 5 input vectors into two
% categories.
%
% Copyright 1992-2002 The MathWorks, Inc.
% $Revision: 1.
www.eeworm.com/read/286662/8751650
m cascade_correlation.m
function [test_targets, Wh, Wo, J] = Cascade_Correlation(train_patterns, train_targets, test_patterns, params)
% Classify using a backpropagation network with the cascade-correlation algorithm
% I
www.eeworm.com/read/286662/8751673
m multivariate_splines.m
function test_targets = Multivariate_Splines(train_patterns, train_targets, test_patterns, params)
% Classify using multivariate adaptive regression splines
% Inputs:
% train_patterns - Train pa
www.eeworm.com/read/286662/8751713
m perceptron_batch.m
function [test_targets, a, updates] = Perceptron_Batch(train_patterns, train_targets, test_patterns, params)
% Classify using the batch Perceptron algorithm
% Inputs:
% train_patterns - Train pa
www.eeworm.com/read/286662/8751727
m perceptron_voted.m
function test_targets = Perceptron_Voted(train_patterns, train_targets, test_patterns, params)
% Classify using the Voted Perceptron algorithm
% Inputs:
% train_patterns - Train patterns
% trai
www.eeworm.com/read/286662/8751748
m optimal_brain_surgeon.m
function [test_targets, Wh, Wo, J] = Optimal_Brain_Surgeon(train_patterns, train_targets, test_patterns, params)
% Classify using a backpropagation network with a batch learning algorithm and remov
www.eeworm.com/read/286662/8751767
m relaxation_bm.m
function [test_targets, a] = Relaxation_BM(train_patterns, train_targets, test_patterns, params)
% Classify using the batch relaxation with margin algorithm
% Inputs:
% train_patterns - Train pa
www.eeworm.com/read/286662/8751886
m locboost.m
function [test_targets, P, theta, phi] = LocBoost(train_patterns, train_targets, test_patterns, params)
% Classify using the local boosting algorithm
% Inputs:
% train_patterns - Train patterns