代码搜索：patterns
找到约 8,017 项符合「patterns」的源代码
代码结果 8,017
www.eeworm.com/read/135035/13966270
bp sonar.bp
* This program runs the aspect-angle dependent data from Gorman and
* Sejnowski's article: "Analysis of Hidden Units in a Layered Network
* Trained to Classify Sonar Targets", in Neural Networks, vo
www.eeworm.com/read/286662/8751607
m ls.m
function [test_targets, w] = LS(train_patterns, train_targets, test_patterns, weights)
% Classify using the least-squares algorithm
% Inputs:
% train_patterns - Train patterns
% train_targets
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/286662/8751868
m components_without_df.m
function [test_targets, errors] = Components_without_DF(train_patterns, train_targets, test_patterns, Classifiers)
% Classify points using component classifiers without discriminant functions
% In
www.eeworm.com/read/286662/8751899
m fisherslineardiscriminant.m
function [patterns, train_targets, w] = FishersLinearDiscriminant(train_patterns, train_targets, param, plot_on)
%Reshape the data points using the Fisher's linear discriminant
%Inputs:
% train_p
www.eeworm.com/read/286662/8752039
m ml_ii.m
function test_targets = ML_II(train_patterns, train_targets, test_patterns, Ngaussians)
% Classify using the ML-II algorithm. This function accepts as inputs the maximum number
% of Gaussians per
www.eeworm.com/read/372113/9521068
m ls.m
function [test_targets, w] = LS(train_patterns, train_targets, test_patterns, weights)
% Classify using the least-squares algorithm
% Inputs:
% train_patterns - Train patterns
% train_targets
www.eeworm.com/read/372113/9521126
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/372113/9521247
m components_without_df.m
function [test_targets, errors] = Components_without_DF(train_patterns, train_targets, test_patterns, Classifiers)
% Classify points using component classifiers without discriminant functions
% In
www.eeworm.com/read/372113/9521278
m fisherslineardiscriminant.m
function [patterns, train_targets, w] = FishersLinearDiscriminant(train_patterns, train_targets, param, plot_on)
%Reshape the data points using the Fisher's linear discriminant
%Inputs:
% train_p