一些支持向量机（SVM）的开源代码库的链接及其简介

出处：http://blog.csdn.net/carson2005/article/details/8586201

（1）LIBSVM:     http://www.csie.ntu.edu.tw/~cjlin/libsvm/

LIBSVM is an integrated software for support vector classification, (C-SVC, nu-SVC), regression (epsilon-SVR, nu-SVR) and distribution estimation (one-class SVM). It supports multi-class classification.

Since version 2.8, it implements an SMO-type algorithm proposed in this paper:
R.-E. Fan, P.-H. Chen, and C.-J. Lin. Working set selection using second order information for training SVM. Journal of Machine Learning Research 6, 1889-1918, 2005. You can also find a pseudo code there. (how to cite LIBSVM)

Our goal is to help users from other fields to easily use SVM as a tool. LIBSVM provides a simple interface where users can easily link it with their own programs. Main features of LIBSVM include

• Different SVM formulations
• Efficient multi-class classification
• Cross validation for model selection
• Probability estimates
• Various kernels (including precomputed kernel matrix)
• Weighted SVM for unbalanced data
• Both C++ and Java sources
• GUI demonstrating SVM classification and regression
• Python, R, MATLAB, Perl, Ruby, Weka, Common LISP, CLISP, Haskell, LabVIEW, and PHP interfaces. C# .NET code and CUDA extension is available. 
  It's also included in some data mining environments: RapidMiner, PCP, and LIONsolver.
• Automatic model selection which can generate contour of cross valiation accuracy.

(2)LIBLINEAR   http://www.csie.ntu.edu.tw/~cjlin/liblinear/

LIBLINEAR is a linear classifier for data with millions of instances and features. It supports

• L2-regularized classifiers 
  L2-loss linear SVM, L1-loss linear SVM, and logistic regression (LR)
• L1-regularized classifiers (after version 1.4) 
  L2-loss linear SVM and logistic regression (LR)
• L2-regularized support vector regression (after version 1.9) 
  L2-loss linear SVR and L1-loss linear SVR.

Main features of LIBLINEAR include

• Same data format as LIBSVM, our general-purpose SVM solver, and also similar usage
• Multi-class classification: 1) one-vs-the rest, 2) Crammer & Singer
• Cross validation for model selection
• Probability estimates (logistic regression only)
• Weights for unbalanced data
• MATLAB/Octave, Java, Python, Ruby interfaces

(3)SVMlight   http://www.cs.cornell.edu/People/tj/svm_light/

SVM^light is an implementation of Vapnik's Support Vector Machine [Vapnik, 1995] for the problem of pattern recognition, for the problem of regression, and for the problem of learning a ranking function. The optimization algorithms used in SVM^light are described in [Joachims, 2002a ]. [Joachims, 1999a]. The algorithm has scalable memory requirements and can handle problems with many thousands of support vectors efficiently.

The software also provides methods for assessing the generalization performance efficiently. It includes two efficient estimation methods for both error rate and precision/recall. XiAlpha-estimates [Joachims, 2002a, Joachims, 2000b] can be computed at essentially no computational expense, but they are conservatively biased. Almost unbiased estimates provides leave-one-out testing. SVM^light exploits that the results of most leave-one-outs (often more than 99%) are predetermined and need not be computed [Joachims, 2002a].

New in this version is an algorithm for learning ranking functions [Joachims, 2002c]. The goal is to learn a function from preference examples, so that it orders a new set of objects as accurately as possible. Such ranking problems naturally occur in applications like search engines and recommender systems.

Futhermore, this version includes an algorithm for training large-scale transductive SVMs. The algorithm proceeds by solving a sequence of optimization problems lower-bounding the solution using a form of local search. A detailed description of the algorithm can be found in [Joachims, 1999c]. A similar transductive learner, which can be thought of as a transductive version of k-Nearest Neighbor is the Spectral Graph Transducer.

SVM^light can also train SVMs with cost models (see [Morik et al., 1999]).

The code has been used on a large range of problems, including text classification [Joachims, 1999c][Joachims, 1998a], image recognition tasks, bioinformatics and medical applications. Many tasks have the property of sparse instance vectors. This implementation makes use of this property which leads to a very compact and efficient representation.

(4)SVM struct    http://www.cs.cornell.edu/People/tj/svm_light/svm_struct.html

SVM^struct is a Support Vector Machine (SVM) algorithm for predicting multivariate or structured outputs. It performs supervised learning by approximating a mapping

h: X --> Y

using labeled training examples (x₁,y₁), ..., (x_n,y_n). Unlike regular SVMs, however, which consider only univariate predictions like in classification and regression,SVM^struct can predict complex objects y like trees, sequences, or sets. Examples of problems with complex outputs are natural language parsing, sequence alignment in protein homology detection, and markov models for part-of-speech tagging. The SVM^struct algorithm can also be used for linear-time training of binary and multi-class SVMs under the linear kernel [4].

The 1-slack cutting-plane algorithm implemented in SVM^struct V3.10 uses a new but equivalent formulation of the structural SVM quadratic program and is several orders of magnitude faster than prior methods. The algorithm is described in [5]. The n-slack algorithm of SVM^struct V2.50 is described in [1][2]. The SVM^struct implementation is based on the SVM^light quadratic optimizer 

(5)BSVM    http://www.csie.ntu.edu.tw/~cjlin/bsvm/

BSVM solves support vector machines (SVM) for the solution of large classification and regression problems. It includes the following methods

• One vs. One multi-class classification using a bound-constrained formulation
• Multi-class classification by solving a single optimization problem (again, a bounded formulation). See Section 3 of our comparison paper.
• Multi-class classification using Crammer and Singer's formulation. See Section 4 of our comparison paper.
• Regression using a bound-constrained formulation
• Multi-class classification using Crammer and Singer's formulation with squared hinge (L2) loss

The current implementation borrows the structure of libsvm. Similar options are also adopted. For the bound-constrained formulation for classification and regression, BSVM uses a decomposition method. BSVM uses a simple working set selection which leads to faster convergences for difficult cases. The use of a special implementation of the opmization solver TRON allows BSVM to stably identify bounded variables.

(6)M-SVM    http://www.loria.fr/~guermeur/

multi class SVM implementation in c by Guermeur

(7)MATLAB SVM toolbox    http://www.isis.ecs.soton.ac.uk/resources/svminfo/

The toolbox provides routines for support vector classification and support vector regression. A GUI is included which allows the visualisation of simple classification and regression problems. (The MATLAB optimisation toolbox, or an alternative quadratic programming routine is required.)

(8)TinySVM    http://chasen.org/~taku/software/TinySVM/

TinySVM is an implementation of Support Vector Machines (SVMs) [Vapnik 95], [Vapnik 98] for the problem of pattern recognition. Support Vector Machines is a new generation learning algorithms based on recent advances in statistical learning theory, and applied to large number of real-world applications, such as text categorization, hand-written character recognition.

(9)GPDT   http://dm.unife.it/gpdt/

GPDT is a C++ software designed to train large-scale Support Vector Machines (SVMs) for binary classification in both scalar and distributed memory parallel environments. It uses a popular problem decomposition technique [1, 2, 4, 6, 7] to split the SVM quadratic programming (QP) problem into a sequence of smaller QP subproblems, each one being solved by a suitable gradient projection method (GPM). The currently implemented GPMs are the Generalized Variable Projection Method (GVPM) [3] and the Dai-Fletcher method (DFGPM) [5].

A few minor bugs fixed (see more details in the CHANGES file, also packaged with the sources distribution) 
[Last updated: Fabruary 7, 2007.]

(10)Spider   http://people.kyb.tuebingen.mpg.de/spider/

The spider is intended to be a complete object orientated environment for machine learning in Matlab. Aside from easy use of base learning algorithms, algorithms can be plugged together and can be compared with, e.g model selection, statistical tests and visual plots. This gives all the power of objects (reusability, plug together, share code) but also all the power of Matlab for machine learning research.

(11)HeroSVM    http://www.cenparmi.concordia.ca/~jdong/HeroSvm.html

Support Vector Machine (SVM)  represents the state-of-the-art classification technique. However, training SVM on a large training set becomes a bottleneck. HeroSvm is a high-performance library for training SVM for classification to solve this problem. It  has been implemented based on our proposed method [1][5][22].  In order to facilitate the software portability and maintenance, an object-oriented method has been applied to design the package. Error handling is supported and HeroSvm is exception-safe. HeroSvm is written in C++. In the current version, a dynamic link library in windows or a shared library in linux is provided to train SVM on a large-scale learning problem efficiently for research purpose in PC platform. We expect that HeroSVM can facilitate the training of support vector machine and solve some real-world problems in various engineering fields.

(12)LS-SVMlab   http://www.esat.kuleuven.be/sista/lssvmlab/

Support Vector Machines is a powerful methodology for solving problems in nonlinear classification, function estimation and density estimation which has also led to many other recent developments in kernel based methods in general. Originally, it has been introduced within the context of statistical learning theory and structural risk minimization. In the methods one solves convex optimization problems, typically quadratic programs. Least Squares Support Vector Machines (LS-SVM) are reformulations to the standard SVMs which lead to solving linear KKT systems. LS-SVMs are closely related to regularization networks and Gaussian processes but additionally emphasize and exploit primal-dual interpretations. Links between kernel versions of classical pattern recognition algorithms such as kernel Fisher discriminant analysis and extensions to unsupervised learning, recurrent networks and control are available. Robustness, sparseness and weightings can be incorporated into LS-SVMs where needed and a Bayesian framework with three levels of inference has been developed. LS-SVM based primal-dual formulations have been given to kernel PCA, kernel CCA and kernel PLS. Recent developments are in kernel spectral clustering, data visualization and dimensionality reduction, and survival analysis. For very large scale problems a method of Fixed Size LS-SVM is proposed. The present LS-SVMlab toolbox contains Matlab/C implementations for a number of LS-SVM algorithms.

(13)LSVM   http://research.cs.wisc.edu/dmi/lsvm/

LSVM is a fast technique for training support vector machines (SVMs), based on a simple iterative approach. For example, it has been used to classify a dataset with 2 million points and 10 features in only 34 minutes on a 400 Mhz Pentium II. For more information, see our paper Lagrangian Support Vector Machines.

SVMs are optimization based tools for solving machine learning problems. For an introduction to SVMs, you may want to look at this tutorial.

The software is free for academic and research use. For commercial use, please contact Olvi Mangasarian or Dave Musicant.

Click here to download the software, which consists of MATLAB m-files.

If you publish any work based on LSVM, please cite both the software and the paper on which it is based. Here are recommended LaTeX bibliography entries:

@misc{lsvm,
author = "O.L. Mangasarian and D. R. Musicant",
title = {{LSVM Software:} Active Set Support Vector Machine Classification Software},
year = 2000,
institution = {Computer Sciences Department, University of Wisconsin, Madison},
note = { www.cs.wisc.edu/$\sim$musicant/lsvm/.}}

@techreport{mm:00,
author = "O. L. Mangasarian and David R. Musicant",
title = "Lagrangian Support Vector Machine Classification",
institution = "Data Mining Institute, Computer Sciences Department, University of Wisconsin",
month = {June},
year = 2000,
number = {00-06},
address = "Madison, Wisconsin",
note={ftp://ftp.cs.wisc.edu/pub/dmi/tech-reports/00-06.ps}}

For more information, contact:
Olvi L. Mangasarian
[email protected]
David R. Musicant
[email protected]

(14)ASVM    http://research.cs.wisc.edu/dmi/asvm/

ASVM is a fast technique for training linear support vector machines (SVMs), based on an active set approach which results in very fast running times. For example, it has been used to classify a dataset with 4 million points and 32 features in only 38 minutes on a 400 Mhz Pentium II. For more information, see our paper Active Support Vector Machines.

SVMs are an optimization based approach for solving machine learning problems. For an introduction to SVMs, you may want to look at this tutorial.

The software is free for academic use. For commercial use, please contact Dave Musicant.

Click here to download the software. The software consists of:

• A stand-alone executable to do training
• A stand-alone executable to do testing
• A mexfile for use in the MATLAB environment

No additional software whatsoever is required to use these tools.

If you publish any work based on ASVM, please cite both the software and the paper on which it is based. Here are recommended LaTeX bibliography entries:

@misc{asvm,
author = "D. R. Musicant",
title = {{ASVM Software:} Active Set Support Vector Machine Classification Software},
year = 2000,
institution = {Computer Sciences Department, University of Wisconsin, Madison},
note = { www.cs.wisc.edu/$\sim$musicant/asvm/.}}

@techreport{mm:00,
author = "O. L. Mangasarian and David R. Musicant",
title = "Active Support Vector Machine Classification",
institution = "Data Mining Institute, Computer Sciences Department, University of Wisconsin",
month = {April},
year = 2000,
number = {00-04},
address = "Madison, Wisconsin",
note={ftp://ftp.cs.wisc.edu/pub/dmi/tech-reports/00-04.ps}}

For more information, contact:
David R. Musicant
[email protected]

(15)psvm    http://research.cs.wisc.edu/dmi/svm/psvm/

Iinstead of a standard support vector machine that classifies points by assigning them to one of two disjoint half-spaces, PSVM classifies points by assigning them to the closest of two parallel planes. For more information, see our paper Proximal Support Vector Machines.

SVMs are an optimization based approach for solving machine learning problems. For an introduction to SVMs, you may want to look at this tutorial.

The software is free for academic use. For commercial use, please contact Olvi Mangasarian.

Click here to download the software. The software consists of:

• A linear version of the PSVM
• A nonlinear version of the PSVM

The only software needed to run these programs is MATLAB www.mathworks.com.

(16)Linear SVM   http://linearsvm.com/

Linear SVM is the newest extremely fast machine learning (data mining) algorithm for solving multiclassclassification problems from ultra large data sets that implements an original proprietary version of acutting plane algorithm for designing a linear support vector machine. LinearSVM is a linearly scalable routine meaning that it creates an SVM model in a CPU time which scales linearly with the size of the training data set. Our comparisons with other known SVM models clearly show its superior performance when high accuracy is required. We would highly appreciate if you may share LinearSVM performance on your data sets with us.