代码搜索：Learning
找到约 5,352 项符合「Learning」的源代码
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www.eeworm.com/read/412367/11202060
m readme.m
This is a parsed code (MATLAB P files) version of the simulation routines accompanying
Vojislav KECMAN's Book:
LEARNING AND SOFT COMPUTING
Support Vect
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m read about simulational experiments.m
Vojislav KECMAN's Book:
LEARNING AND SOFT COMPUTING
Support Vector Machines, Neural Networks and Fuzzy Logic Models
The MIT Press, Cambridge, MA, 2000
ISBN 0
www.eeworm.com/read/411382/11247785
m hop_stor.m
function W=hop_stor(P)
% function W=hop_stor(P)
%
% performs the storage (learning phase) for a Hopfield network
%
% W - weight matrix
% P - patterns to be stored (column wise matrix)
%
% Hugh
www.eeworm.com/read/146896/12605412
plg 矩阵相乘.plg
Build Log
--------------------Configuration: 矩阵相乘 - Win32 Debug--------------------
Command Lines
Creating temporary file "C:\DOCUME~1\scyfm\LO
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bbl manual.bbl
\begin{thebibliography}{}
\bibitem[Boser et~al., 1992]{Boser1992}
Boser, B., Guyon, I., and Vapnik, V.~N. (1992).
\newblock A training algorithm for optimal margin classifiers.
\newblock In {\em
www.eeworm.com/read/389844/8496291
asv demo_incremental.asv
%~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
%
% Incremental ELM learning DEMO
%
% Author: Povilas Daniu餴s, paralax@hacker.lt
% http://ai.hacker.lt - lithuanian
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m example22a.m
%perc2a
%%===============
%%===============
%
figure('name','训练过程图示','numbertitle','off');
P=[-0.5 -0.5 0.3 0;-0.5 0.5 -0.5 1];
T=[1 1 0 0];
%initialization
[R,Q]=size(P); [S,Q]=size(T)
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m example22.m
%perc2
%%===============
%%===============
%
figure('name','训练过程图示','numbertitle','off');
P=[-0.5 -0.5 0.3 0;-0.5 0.5 -0.5 1];
T=[1 1 0 0];
%initialization
[R,Q]=size(P); [S,Q]=size(T);
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m example24a.m
%perc4
%%===============
%%===============
figure('name','训练过程图示','numbertitle','off');
P=[-0.5 -0.5 0.3 0 -0.8;-0.5 0.5 -0.5 1 0];
T=[1 1 0 0 0];
%initialization
[R,Q]=size(P); [S,Q]=size(T
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m selforganize.m
function [w,wbias,y,d,b,sse]=selforganize(x,c,t)
% RBF网络的实现
%x为np×ni的输入矩阵。np为输入样本个数,ni为RBF网络输入层单元数
%c为ni×m的初始中心矩阵。m为中心的个数
%t为np×no的期望输出矩阵。No为RBF网络输出层节单元数
[np,ni]=size(x);
d=learning_c(x,c); %学