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/474600/6813448
m stochastic_sa.m
function [patterns, targets] = Stochastic_SA(train_patterns, train_targets, params, plot_on)
%Reduce the number of data points using the stochastic simulated annealing algorithm
%Inputs:
% train_
www.eeworm.com/read/474600/6813549
m sohc.m
function [patterns, targets, label] = SOHC(train_patterns, train_targets, Nmu, plot_on)
%Reduce the number of data points using the stepwise optimal hierarchical clustering algorithm
%Inputs:
% t
www.eeworm.com/read/474600/6813561
m lvq3.m
function [patterns, targets] = LVQ3(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/474600/6813576
m components_with_df.m
function [test_targets, errors] = Components_with_DF(train_patterns, train_targets, test_patterns, Ncomponents)
% Classify points using component classifiers with discriminant functions
% Inputs:
www.eeworm.com/read/166148/10031942
ps a dynamic lookup scheme for bursty access patterns_infocom2001.ps
%!PS-Adobe-2.0
%%Creator: dvips 5.55 Copyright 1986, 1994 Radical Eye Software
%%Title: infocom.dvi
%%CreationDate: Fri Jan 12 16:38:51 2001
%%Pages: 10
%%PageOrder: Ascend
%%BoundingBox: 0 0 612 792
www.eeworm.com/read/15354/449581
pdf wiley.ieee.press.communication.patterns.of.engineers.ebook-lib.pdf
www.eeworm.com/read/105943/15652907
url design patterns in java - java - research - david wallace croft.url
[DEFAULT]
BASEURL=http://www.alumni.caltech.edu/~croft/research/java/pattern/
[InternetShortcut]
URL=http://www.alumni.caltech.edu/~croft/research/java/pattern/
Modified=80C2BB2F6866C001BB
www.eeworm.com/read/286662/8751641
m nearestneighborediting.m
function [patterns, targets] = NearestNeighborEditing(train_patterns, train_targets, Nmu, plot_on)
%Reduce the number of data points using the nearest neighbor editing algorithm
%Inputs:
% train_
www.eeworm.com/read/286662/8751734
m addc.m
function [patterns, targets] = ADDC(train_patterns, train_targets, Nmu, plot_on)
%Reduce the number of data points using the Agglomerative clustering algorithm
%Inputs:
% train_patterns - Input p
www.eeworm.com/read/286662/8751756
m nddf.m
function [test_targets, g0, g1] = NDDF(train_patterns, train_targets, test_patterns, cost)
% Classify using the normal density discriminant function
% Inputs:
% train_patterns - Train patterns