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/458224/7301658
windows gpn_ada.m.windows
% Simulate a goal programming network
% Adaptive learning rate strategy has been used (cfr SuperSAB)
%
% Usage: [V,F,E,U,M,conv_flag] = gpn_ada (D, B, G, ep, dt, U_init, m_init)
%
% V Final Network S
www.eeworm.com/read/242043/13098105
cpp stringtest.cpp
#include
#include
#include
#include "String2.h" //包含顺序串类
void main(void)
{
String str1("Data Structrure"), str2("Learning "); //用构造函数1
String str
www.eeworm.com/read/312163/13617636
m qpssvm.m
function [x,fval,stat] = qpssvm(H,f,b,I,x0,options)
% QPSSVM Solves QP task required for StructSVM learning.
%
% Synopsis:
% [x,fval,stat] = qpssvm(H,f,b,I)
% [x,fval,stat] = qpssvm(H,f,b,I,x0)
% [
www.eeworm.com/read/221812/14719910
cpp stringtest.cpp
#include
#include
#include
#include "String2.h" //包含顺序串类
void main(void)
{
String str1("Data Structrure"), str2("Learning "); //用构造函数1
String str
www.eeworm.com/read/221024/14761251
html http:^^www.cs.wisc.edu^~shavlik^cs760.html
Date: Mon, 11 Nov 1996 02:51:58 GMT
Server: NCSA/1.5
Content-type: text/html
Last-modified: Mon, 29 Apr 1996 19:08:10 GMT
Content-length: 12276
CS 760 - Machine Learning
www.eeworm.com/read/187736/5217740
java layer.java
package net.openai.ai.nn.network;
import java.io.*;
import java.util.*;
import net.openai.ai.nn.learning.*;
import net.openai.ai.nn.transfer.*;
import net.openai.ai.nn.training.*;
import net.openai.a
www.eeworm.com/read/187736/5217741
java network.java
package net.openai.ai.nn.network;
import java.util.*;
import java.io.*;
import net.openai.ai.nn.architecture.*;
import net.openai.ai.nn.error.*;
import net.openai.ai.nn.learning.*;
import net.openai
www.eeworm.com/read/367675/2833479
txt 18.txt
发信人: minus (qq), 信区: DataMining
标 题: Perhaps a useful URL
发信站: 南京大学小百合站 (Mon Apr 21 11:35:14 2003)
http://jmlg.org/
Journal of Machine Learning Gossip
Mission Statement
The mission
www.eeworm.com/read/160391/5571139
m mcmc1.m
% We compare MCMC structure learning with exhaustive enumeration of all dags.
N = 3;
%N = 4;
dag = mk_rnd_dag(N);
ns = 2*ones(1,N);
bnet = mk_bnet(dag, ns);
for i=1:N
bnet.CPD{i} = tabular_
www.eeworm.com/read/473057/6854441
m prune_tree_c45.m
function T = prune_tree_C45(T,A,B,CF)
% PRUNE_TREE_C45 prunes the decision tree T using the pruning algorithm from
% C4.5: Programs from Machine Learning.
%
% T = prune_tree_C45(T,A,B,CF)
%
% T