文件名称:libsvm-2.9
- 所属分类：
- 图形图像处理(光照,映射..)
- 资源属性：
- [MacOS] [C/C++] [源码]
- 上传时间：
- 2012-11-26
- 文件大小：
- 656kb
- 下载次数：
- 0次
- 提 供 者：
- 王**
- 相关连接：
- 无
- 下载说明：
- 别用迅雷下载，失败请重下，重下不扣分！
介绍说明－－下载内容均来自于网络，请自行研究使用
LIBSVM是台湾大学林智仁(Lin Chih-Jen)副教授等开发设计的一个简单、易于使用和快速有效的SVM模式识别与回归的软件包，他不但提供了编译好的可在Windows系列系统的执行文件，还提供了源代码，方便改进、修改以及在其它操作系统上应用；该软件对SVM所涉及的参数调节相对比较少，提供了很多的默认参数，利用这些默认参数可以解决很多问题；并提供了交互检验(Cross Validation)的功能。该软件包可在http://www.csie.ntu.edu.tw/~cjlin/免费获得。该软件可以解决C-SVM、ν-SVM、ε-SVR和ν-SVR等问题，包括基于一对一算法的多类模式识别问题-The LIBSVM Is Taiwan University Chih- Jen Lin (Lin Chih-Jen) Associate Professor of the development and design of a simple, easy to use and fast and efficient SVM pattern recognition and regression package, he not only compiled perform file system in the Windows series also provides source code to facilitate the improvement modifications and applications on other operating systems the software adjust the parameters involved in SVM is relatively small, a lot of the default parameters, use these default parameters can solve a lot of problems and provide cross-validation (Cross Validation) function. The package can http://www.csie.ntu.edu.tw/ ~ cjlin/free access. The software can solve the problem of the C-SVM, ν-SVM, ε-SVR and ν-SVR, including the multi-class pattern recognition problem based on a one-to-one algorithm
(系统自动生成,下载前可以参看下载内容)
下载文件列表
libsvm-2.9\COPYRIGHT
..........\FAQ.html
..........\heart_scale
..........\java\libsvm\svm.java
..........\....\......\svm.m4
..........\....\......\svm_model.java
..........\....\......\svm_node.java
..........\....\......\svm_parameter.java
..........\....\......\svm_print_interface.java
..........\....\......\svm_problem.java
..........\....\libsvm.jar
..........\....\Makefile
..........\....\svm_predict.java
..........\....\svm_scale.java
..........\....\svm_toy.java
..........\....\svm_train.java
..........\....\test_applet.html
..........\Makefile
..........\Makefile.win
..........\python\cross_validation.py
..........\......\Makefile
..........\......\README
..........\......\setup.py
..........\......\svm.py
..........\......\svmc.i
..........\......\svmc_wrap.c
..........\......\svm_test.py
..........\......\test_cross_validation.py
..........\README
..........\svm-predict.c
..........\svm-scale.c
..........\....toy\gtk\callbacks.cpp
..........\.......\...\callbacks.h
..........\.......\...\interface.c
..........\.......\...\interface.h
..........\.......\...\main.c
..........\.......\...\Makefile
..........\.......\...\svm-toy.glade
..........\.......\qt\Makefile
..........\.......\..\svm-toy.cpp
..........\.......\windows\svm-toy.cpp
..........\svm-train.c
..........\svm.cpp
..........\svm.h
..........\tools\checkdata.py
..........\.....\easy.py
..........\.....\grid.py
..........\.....\README
..........\.....\subset.py
..........\windows\python\svmc.pyd
..........\.......\svm-predict.exe
..........\.......\svm-scale.exe
..........\.......\svm-toy.exe
..........\.......\svm-train.exe
..........\使用方法\LIBSVM使用方法.pdf
..........\........\如何使用.txt
..........\java\libsvm
..........\svm-toy\gtk
..........\.......\qt
..........\.......\windows
..........\windows\python
..........\java
..........\python
..........\svm-toy
..........\tools
..........\windows
..........\使用方法
libsvm-2.9