MATLAB_neural_network
所属分类:matlab编程
开发工具:matlab
文件大小:10714KB
下载次数:12
上传日期:2016-03-14 22:20:35
上 传 者:
枭隳
说明: 基于MATLAP的43个神经网络经典案例,源码加详细注释
(Based MATLAP 43 classic case of neural networks, the source added detailed notes)
文件列表:
MATLAB 神经网络43个案例分析源代码\chapter1\BPDLX.m (6159, 2013-08-21)
MATLAB 神经网络43个案例分析源代码\chapter1\chapter1_1.asv (3896, 2013-08-21)
MATLAB 神经网络43个案例分析源代码\chapter1\chapter1_1.m (4030, 2013-08-21)
MATLAB 神经网络43个案例分析源代码\chapter1\data1.mat (93015, 2009-08-29)
MATLAB 神经网络43个案例分析源代码\chapter1\data2.mat (92845, 2009-08-29)
MATLAB 神经网络43个案例分析源代码\chapter1\data3.mat (92937, 2009-08-29)
MATLAB 神经网络43个案例分析源代码\chapter1\data4.mat (93438, 2009-08-29)
MATLAB 神经网络43个案例分析源代码\chapter10\chapter10.m (1452, 2013-09-02)
MATLAB 神经网络43个案例分析源代码\chapter10\class.mat (443, 2009-10-06)
MATLAB 神经网络43个案例分析源代码\chapter10\sim.mat (465, 2009-10-06)
MATLAB 神经网络43个案例分析源代码\chapter10\stdlib.m (249, 2013-09-02)
MATLAB 神经网络43个案例分析源代码\chapter10\test.m (807, 2013-09-02)
MATLAB 神经网络43个案例分析源代码\chapter11\city_location.mat (232, 2009-09-21)
MATLAB 神经网络43个案例分析源代码\chapter11\diff_u.m (217, 2009-12-21)
MATLAB 神经网络43个案例分析源代码\chapter11\energy.m (247, 2010-01-30)
MATLAB 神经网络43个案例分析源代码\chapter11\main.m (2674, 2013-09-02)
MATLAB 神经网络43个案例分析源代码\chapter12\Chapter_ClassifyRegressUsingLibsvm.m (2555, 2013-08-18)
MATLAB 神经网络43个案例分析源代码\chapter12\heart_scale.mat (28904, 2005-03-22)
MATLAB 神经网络43个案例分析源代码\chapter12\html\Chapter_ClassifyRegressUsingLibsvm.html (14895, 2013-08-18)
MATLAB 神经网络43个案例分析源代码\chapter12\html\Chapter_ClassifyRegressUsingLibsvm.png (1911, 2013-08-18)
MATLAB 神经网络43个案例分析源代码\chapter12\html\Chapter_ClassifyRegressUsingLibsvm_01.png (9183, 2013-08-18)
MATLAB 神经网络43个案例分析源代码\chapter13\Chapter_ModelDecryption.m (1308, 2013-08-18)
MATLAB 神经网络43个案例分析源代码\chapter13\heart_scale.mat (28904, 2005-03-22)
MATLAB 神经网络43个案例分析源代码\chapter13\html\Chapter_ModelDecryption.html (8933, 2013-08-18)
MATLAB 神经网络43个案例分析源代码\chapter14\chapter_WineClass.m (2429, 2013-08-18)
MATLAB 神经网络43个案例分析源代码\chapter14\chapter_WineClass.mat (20168, 2010-01-30)
MATLAB 神经网络43个案例分析源代码\chapter14\html\chapter_WineClass.html (13636, 2013-08-18)
MATLAB 神经网络43个案例分析源代码\chapter14\html\chapter_WineClass.png (3346, 2013-08-18)
MATLAB 神经网络43个案例分析源代码\chapter14\html\chapter_WineClass_01.png (6819, 2013-08-18)
MATLAB 神经网络43个案例分析源代码\chapter14\html\chapter_WineClass_02.png (10865, 2013-08-18)
MATLAB 神经网络43个案例分析源代码\chapter14\html\chapter_WineClass_03.png (9098, 2013-08-18)
MATLAB 神经网络43个案例分析源代码\chapter15\chapter_GA.m (6450, 2013-08-18)
MATLAB 神经网络43个案例分析源代码\chapter15\chapter_GridSearch.m (6042, 2013-08-18)
MATLAB 神经网络43个案例分析源代码\chapter15\chapter_PSO.m (8453, 2013-08-18)
MATLAB 神经网络43个案例分析源代码\chapter15\html\chapter_GA.html (25844, 2013-08-18)
MATLAB 神经网络43个案例分析源代码\chapter15\html\chapter_GA.png (3346, 2013-08-18)
MATLAB 神经网络43个案例分析源代码\chapter15\html\chapter_GA_01.png (6819, 2013-08-18)
MATLAB 神经网络43个案例分析源代码\chapter15\html\chapter_GA_02.png (10865, 2013-08-18)
... ...
SVM faruto version
by faruto
Email:farutoliyang@gmail.com
2009.11.05
==================================
Content:
scaleForSVM:归一化
函数接口:
[train_scale,test_scale,ps] = scaleForSVM(train_data,test_data,ymin,ymax)
====================================
pcaForSVM:pca降维预处理
函数接口:
[train_pca,test_pca] = pcaForSVM(train,test,threshold)
====================================
fasticaForSVM:ica降维预处理
函数接口:
[train_ica,test_ica] = fasticaForSVM(train,test)
====================================
SVMcgForClass:分类问题参数寻找[grid search based on CV]
函数接口:
[bestacc,bestc,bestg] = SVMcgForClass(train_label,train,cmin,cmax,gmin,gmax,v,cstep,gstep,accstep)
SVMcgForRegress:回归问题参数寻优[grid search based on CV]
函数接口:
[mse,bestc,bestg] = SVMcgForRegress(train_label,train,cmin,cmax,gmin,gmax,v,cstep,gstep,msestep)
======================================
psoSVMcgForClass:分类问题参数寻优[pso based on CV]
函数接口:
[bestCVaccuracy,bestc,bestg,pso_option] = psoSVMcgForClass(train_label,train,pso_option)
psoSVMcgForRegress:回归问题参数寻优[pso based on CV]
函数接口:
[bestCVmse,bestc,bestg,pso_option] = psoSVMcgForRegress(train_label,train,pso_option)
=======================================
gaSVMcgForClass:分类问题参数寻优[ga based on CV]
函数接口:
[bestCVaccuracy,bestc,bestg,ga_option] = gaSVMcgForClass(train_label,train,ga_option)
gaSVMcgForRegress:回归问题参数寻优[ga based on CV]
函数接口:
[bestCVmse,bestc,bestg,ga_option] = gaSVMcgForRegress(train_label,train,ga_option)
======================================
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