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/405069/11472268
m store_grabbag.m
function test_targets = Store_Grabbag(train_patterns, train_targets, test_patterns, Knn)
% Classify using the store-grabbag algorithm (an improvement on the nearest neighbor)
% Inputs:
% train_p
www.eeworm.com/read/474600/6813524
m store_grabbag.m
function test_targets = Store_Grabbag(train_patterns, train_targets, test_patterns, Knn)
% Classify using the store-grabbag algorithm (an improvement on the nearest neighbor)
% Inputs:
% train_p
www.eeworm.com/read/286662/8751706
m min_spanning_tree.m
function [patterns, targets] = min_spanning_tree(train_patterns, train_targets, params, plot_on)
%Reduce the number of data points using a spanning tree
%Inputs:
% train_patterns - Input patterns
www.eeworm.com/read/286662/8751731
m interactive_learning.m
function test_targets = Interactive_Learning(train_patterns, train_targets, test_patterns, params)
% Classify using nearest neighbors and interactive learning
% Inputs:
% train_patterns - Train
www.eeworm.com/read/286662/8751742
m bimsec.m
function [patterns, targets, label, J] = BIMSEC(train_patterns, train_targets, params, plot_on)
%Reduce the number of data points using the basic iterative MSE clustering algorithm
%Inputs:
% tra
www.eeworm.com/read/286662/8751783
m lvq1.m
function [patterns, targets] = LVQ1(train_patterns, train_targets, Nmu, plot_on)
%Reduce the number of data points using linear vector quantization
%Inputs:
% train_patterns - Input patterns
% t
www.eeworm.com/read/286662/8751859
m dslvq.m
function [patterns, targets, w] = DSLVQ(train_patterns, train_targets, Nmu, plot_on)
%Reduce the number of data points using distinction sensitive linear vector quantization
%Inputs:
% train_pat
www.eeworm.com/read/286662/8751965
m deterministic_annealing.m
function [patterns, targets] = deterministic_annealing(train_patterns, train_targets, params, plot_on)
%Reduce the number of data points using the deterministic annealing algorithm
%Inputs:
% tra
www.eeworm.com/read/286662/8751996
m marginalization.m
function [targets, P] = Marginalization(patterns, targets, params, plot_on)
% Classify data with missing features using the marginal distribution
%
% Inputs:
% patterns - Input patterns
% t
www.eeworm.com/read/372113/9521112
m min_spanning_tree.m
function [patterns, targets] = min_spanning_tree(train_patterns, train_targets, params, plot_on)
%Reduce the number of data points using a spanning tree
%Inputs:
% train_patterns - Input patterns