文件名称:svm-matlab-
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支持向量机的源代码，可以实现分类和回归分析。-ctions on Intelligent Systems and Technology
ABSTRACT LIBSVM is a library for Support Vector Machines (SVMs). We have been actively developing this package since the year 2000. The goal is to help users to easily apply SVM to their applications. LIBSVM has gained wide popularity in machine learning and many other areas. In this article, we present all implementation details of LIBSVM. Issues such as solving SVM optimization problems theoretical convergence multiclass classification probability estimates and parameter selection are discussed in detail.
we show formulations used in LIBSVM: C-support vector classifica-tion (C-SVC), ν-support vector classification (ν-SVC), distribution estimation (one-class SVM), -support vector regression (-SVR), and ν-support vector regression
(ν-SVR). We discuss the implementation of solving quadratic problems
ABSTRACT LIBSVM is a library for Support Vector Machines (SVMs). We have been actively developing this package since the year 2000. The goal is to help users to easily apply SVM to their applications. LIBSVM has gained wide popularity in machine learning and many other areas. In this article, we present all implementation details of LIBSVM. Issues such as solving SVM optimization problems theoretical convergence multiclass classification probability estimates and parameter selection are discussed in detail.
we show formulations used in LIBSVM: C-support vector classifica-tion (C-SVC), ν-support vector classification (ν-SVC), distribution estimation (one-class SVM), -support vector regression (-SVR), and ν-support vector regression
(ν-SVR). We discuss the implementation of solving quadratic problems
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下载文件列表
libsvm-3.20
...........\COPYRIGHT
...........\FAQ.html
...........\Makefile
...........\Makefile.win
...........\README
...........\heart_scale
...........\java
...........\....\Makefile
...........\....\libsvm
...........\....\......\svm.java
...........\....\......\svm.m4
...........\....\......\svm_model.java
...........\....\......\svm_node.java
...........\....\......\svm_parameter.java
...........\....\......\svm_print_interface.java
...........\....\......\svm_problem.java
...........\....\libsvm.jar
...........\....\svm_predict.java
...........\....\svm_scale.java
...........\....\svm_toy.java
...........\....\svm_train.java
...........\....\test_applet.html
...........\matlab
...........\......\Makefile
...........\......\README
...........\......\libsvmread.c
...........\......\libsvmwrite.c
...........\......\make.m
...........\......\svm_model_matlab.c
...........\......\svm_model_matlab.h
...........\......\svmpredict.c
...........\......\svmtrain.c
...........\python
...........\......\Makefile
...........\......\README
...........\......\svm.py
...........\......\svmutil.py
...........\svm-predict.c
...........\svm-scale.c
...........\svm-toy
...........\.......\gtk
...........\.......\...\Makefile
...........\.......\...\callbacks.cpp
...........\.......\...\callbacks.h
...........\.......\...\interface.c
...........\.......\...\interface.h
...........\.......\...\main.c
...........\.......\...\svm-toy.glade
...........\.......\qt
...........\.......\..\Makefile
...........\.......\..\svm-toy.cpp
...........\.......\windows
...........\.......\.......\svm-toy.cpp
...........\svm-train.c
...........\svm.cpp
...........\svm.def
...........\svm.h
...........\tools
...........\.....\README
...........\.....\checkdata.py
...........\.....\easy.py
...........\.....\grid.py
...........\.....\subset.py
...........\windows
...........\.......\libsvm.dll
...........\.......\libsvmread.mexw64
...........\.......\libsvmwrite.mexw64
...........\.......\svm-predict.exe
...........\.......\svm-scale.exe
...........\.......\svm-toy.exe
...........\.......\svm-train.exe
...........\.......\svmpredict.mexw64
...........\.......\svmtrain.mexw64