代码搜索：Learning
找到约 5,352 项符合「Learning」的源代码
代码结果 5,352
www.eeworm.com/read/148789/12426030
description
Package: kernlab
Version: 0.1-4
Date: 2003-10-30
Title: Kernel Methods Lab
Author: Alexandros Karatzoglou, Alex Smola, Achim Zeileis, Kurt Hornik
Maintainer: Alexandros Karatzoglou
www.eeworm.com/read/227856/14408962
ps ml-toc.ps
%!PS-Adobe-2.0
%%Creator: dvipsk 5.58f Copyright 1986, 1994 Radical Eye Software
%%Title: ml.dvi
%%Pages: 9
%%PageOrder: Ascend
%%BoundingBox: 0 0 612 792
%%EndComments
%DVIPSCommandLine: dvips -o ml.
www.eeworm.com/read/221024/14761459
html http:^^www.cs.utexas.edu^users^mooney^ml-course^welcome.html
MIME-Version: 1.0
Server: CERN/3.0
Date: Monday, 06-Jan-97 19:22:59 GMT
Content-Type: text/html
Content-Length: 1634
Last-Modified: Thursday, 14-Nov-96 16:55:00 GMT
CS 395T: Machine Lea
www.eeworm.com/read/221024/14769776
edu^ http:^^www-ml.cs.umass.edu^
Date: Wed, 20 Nov 1996 19:48:20 GMT
Server: NCSA/1.5
Content-type: text/html
Last-modified: Wed, 06 Mar 1996 18:08:37 GMT
Content-length: 2037
www.eeworm.com/read/292701/8340494
ps ml-toc.ps
%!PS-Adobe-2.0
%%Creator: dvipsk 5.58f Copyright 1986, 1994 Radical Eye Software
%%Title: ml.dvi
%%Pages: 9
%%PageOrder: Ascend
%%BoundingBox: 0 0 612 792
%%EndComments
%DVIPSCommandLine: dvips -o ml.
www.eeworm.com/read/367442/9747937
m multisvmdemo1.m
% Demonstration of multi-class SVM learning.
% Statistical Pattern Recognition Toolbox, Vojtech Franc, Vaclav Hlavac
% (c) Czech Technical University Prague, http://cmp.felk.cvut.cz
% Modifications
%
www.eeworm.com/read/367442/9748007
m~ multisvmdemo1.m~
% Demonstration of multi-class SVM learning.
% loads data
data = load('multisvm1');
% setting SVM parameters
ker='rbf';
arg=1;
C=inf;
% learning SVM classifier
[model]=m2osmo( data.X, data.I, ker,
www.eeworm.com/read/247646/12638234
h prune.h
/*********************************************************************/
/* LINEAR TREE for Supervised Learning */
/* Versao 1.0 (10/12/1997)
www.eeworm.com/read/135779/13899948
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)