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SVMlight
http://svmlight./
SVMlight,
by Joachims, is one of the most widely used SVM classification and
regression package. It has a fast optimization algorithm, can be applied
to very large datasets, and has a very efficient implementation of the
leave-one-out cross-validation. Distributed as C++ source and binaries
for Linux, Windows, Cygwin, and Solaris. Kernels: polynomial, radial
basis function, and neural (tanh).
SVMstruct
http://svmlight./svm_struct.html
SVMstruct,
by Joachims, is an SVM implementation that can model complex
(multivariate) output data y, such as trees, sequences, or sets. These
complex output SVM models can be applied to natural language parsing,
sequence alignment in protein homology detection, and Markov models for
part-of-speech tagging. Several implementations exist: SVMmulticlass,
for multi-class classification; SVMcfg, learns a weighted context free
grammar from examples; SVMalign, learns to align protein sequences from
training alignments; SVMhmm, learns a Markov model from examples. These
modules have straightforward applications in bioinformatics, but one can
imagine significant implementations for cheminformatics, when the
chemical structure is represented as trees or sequences.
mySVM
http://www-ai.cs./SOFTWARE/MYSVM/index.html
mySVM,
by Stefan Rüping, is a C++ implementation of SVM classification and
regression. Available as C++ source code and Windows binaries. Kernels:
linear, polynomial, radial basis function, neural (tanh), anova.
JmySVM
http://www-ai.cs./SOFTWARE/YALE/index.html
JmySVM,
a Java version of mySVM is part of the YaLE (Yet Another Learning
Environment) learning environment.
mySVM/db
http://www-ai.cs./SOFTWARE/MYSVMDB/index.html
mySVM/db
is an efficient extension of mySVM which is designed to run directly
inside a relational database using an internal JAVA engine. It was
tested with an Oracle database, but with small modifications it should
also run on any database offering a JDBC interface. It is especially
useful for large datasets available as relational databases.
LIBSVM
http://www.csie./~cjlin/libsvm/
LIBSVM
(Library for Support Vector Machines), is developed by Chang and Lin
and contains C-classification, ν-classification, ε-regression, and
ν-regression. Developed in C++ and Java, it supports also multi-class
classification, weighted SVM for unbalanced data, cross-validation and
automatic model selection. It has interfaces for Python, R, Splus,
MATLAB, Perl, Ruby, and LabVIEW. Kernels: linear, polynomial, radial
basis function, and neural (tanh).
looms
http://www.csie./~cjlin/looms/
looms,
by Lee and Lin, is a very efficient leave-one-out model selection for
SVM two-class classification. While LOO cross-validation is usually too
time consuming to be performed for large datasets, looms implements
numerical procedures that make LOO accessible. Given a range of
parameters, looms automatically returns the parameter and model with the
best LOO statistics. Available as C source code and Windows binaries.
BSVM
http://www.csie./~cjlin/bsvm/
BSVM,
authored by of Hsu and Lin, provides two implementations of multi-class
classification, together with SVM regression. Available as source code
for UNIX/Linux and as binaries for Windows.
SVMTorch
http://www./learning/SVMTorch.html
SVMTorch,
by Collobert and Bengio, is part of the Torch machine learning library
and implements SVM classification and regression. Distributed as C++
source code or binaries for Linux and Solaris.
Weka
http://www.cs./ml/weka/
Weka
is a collection of machine learning algorithms for data mining tasks.
The algorithms can either be applied directly to a dataset or called
from a Java code. Contains an SVM implementation.
SVM in R
http://cran./src/contrib/Descriptions/e1071.html
This
SVM implementation in R (http://www./) contains
C-classification, n-classification, e-regression, and n-regression.
Kernels: linear, polynomial, radial basis, neural (tanh).
M-SVM
http://www./~guermeur/
Multi-class
SVM implementation in C by Guermeur.
Gist
http://microarray.cpmc./gist/
Gist
is a C implementation of support vector machine classification and
kernel principal components analysis. The SVM part of Gist is available
as an interactive web server at http://svm. and it is a very
convenient option for users that want to experiment with small datasets
(several hundreds patterns). Kernels: linear, polynomial, radial.
MATLAB SVM Toolbox
http://www.isis.ecs./resources/svminfo/
This
SVM MATLAB toolbox, by Gunn, implements SVM classification and
regression with various kernels: linear, polynomial, Gaussian radial
basis function, exponential radial basis function, neural (tanh),
Fourier series, spline, and B spline.
TinySVM
http:///~taku/software/TinySVM/
TinySVM
is a C++ implementation of C-classification and C-regression which uses
sparse vector representation and can handle several ten-thousands of
training examples, and hundred-thousands of feature dimensions.
Distributed as binary/source for Linux and binary for Windows.
SmartLab
http://www.smartlab.dibe./
SmartLab
provides several support vector machines implementations: cSVM, Windows
and Linux implementation of two-classes classification; mcSVM, Windows
and Linux implementation of multi-classes classification; rSVM, Windows
and Linux implementation of regression; javaSVM1 and javaSVM2, Java
applets for SVM classification.
Gini-SVM
http://bach.ece./svm/ginisvm/
Gini-SVM,
by Chakrabartty and Cauwenberghs, is a multi-class probability
regression engine that generates conditional probability distribution as
a solution. Available as source code.
GPDT
http://dm./gpdt/
GPDT,
by Serafini, Zanni, and Zanghirati, is a C++ implementation for
large-scale SVM classification in both scalar and distributed memory
parallel environments. Available as C++ source code and Windows
binaries.
HeroSvm
http://www.cenparmi./~people/jdong/HeroSvm.html
HeroSvm,
by Dong, is developed in C++, implements SVM classification, and is
distributed as a dynamic link library for Windows. Kernels: linear,
polynomial, radial basis function.
Spider
http://www.kyb.tuebingen./bs/people/spider/
Spider
is an object orientated environment for machine learning in MATLAB, for
unsupervised, supervised or semi-supervised machine learning problems,
and includes training, testing, model selection, cross-validation, and
statistical tests. Implements SVM multi-class classification and
regression.
Java applets
http://svm.dcs./
These
SVM classification and SVM regression Java applets were developed by
members of Royal Holloway, University of London and AT&T Speech and
Image Processing Services Research Lab.
LEARNSC
http://www./html/downloads.html
MATLAB
scripts for the book Learning and Soft Computing by Kecman,
implementing SVM classification and regression.
Tree Kernels
http://ai-nlp.info./moschitti/Tree-Kernel.htm
Tree
Kernels, by Moschitti, is an extension of SVMlight, obtained by
encoding tree kernels. Available as binaries for Windows, Linux,
Mac-OSx, and Solaris. Tree kernels are suitable for encoding chemical
structures, and thus this package brings significant capabilities for
cheminformatics applications.
LS-SVMlab
http://www.esat./sista/lssvmlab/
LS-SVMlab,
by Suykens, is a MATLAB implementation of least squares support vector
machines (LS-SVM) which reformulates the standard SVM leading to solving
linear KKT systems. LS-SVM alike primal-dual formulations have been
given to kernel PCA, kernel CCA and kernel PLS, thereby extending the
class of primal-dual kernel machines. 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.
MATLAB SVM Toolbox
http://www.igi./aschwaig/software.html
This
is a MATLAB SVM classification implementation which can handle 1-norm
and 2-norm SVM (linear or quadratic loss functions).
SVM/LOO
http://bach.ece./pub/gert/svm/incremental/
SVM/LOO,
by Cauwenberghs, has a very efficient MATLAB implementation of the
leave-one-out cross-validation.
SVMsequel
http://www./~hdaume/SVMsequel/
SVMsequel,
by Daume III, is a SVM multi-class classification package, distributed
as C source or binaries for Linux or Solaris. Kernels: linear,
polynomial, radial basis function, sigmoid, string, tree, information
diffusion on discrete manifolds.
LSVM
http://www.cs./dmi/lsvm/
LSVM
(Lagrangian Support Vector Machine) is a very fast SVM implementation
in MATLAB by Mangasarian and Musicant. It can classify datasets with
several millions patterns.
ASVM
http://www.cs./dmi/asvm/
ASVM
(Active Support Vector Machine) is a very fast linear SVM script for
MATLAB, by Musicant and Mangasarian, developed for large datasets.
PSVM
http://www.cs./dmi/svm/psvm/
PSVM
(Proximal Support Vector Machine) is a MATLAB script by Fung and
Mangasarian which classifies patterns by assigning them to the closest
of two parallel planes.
OSU SVM Classifier Matlab
Toolbox
http://www.ece./~maj/osu_svm/
This MATLAB
toolbox is based on LIBSVM.
SimpleSVM Toolbox
http://asi./~gloosli/simpleSVM.html
SimpleSVM
Toolbox is a MATLAB implementation of the SimpleSVM algorithm.
SVM Toolbox
http://asi./%7Earakotom/toolbox/index
A
fairly complex MATLAB toolbox, containing many algorithms:
classification using linear and quadratic penalization, multi-class
classification, ε-regression, ν-regression, wavelet kernel, SVM feature
selection.
MATLAB SVM Toolbox
http://theoval.sys./~gcc/svm/toolbox/
Developed
by Cawley, has standard SVM features, together with multi-class
classification and leave-one-out cross-validation.
R-SVM
http://www.biostat./~xzhang/R-SVM/R-SVM.html
R-SVM,
by Zhang and Wong, is based on SVMTorch and is specially designed for
the classification of microarray gene expression data. R-SVM uses SVM
for classification and for selecting a subset of relevant genes
according to their relative contribution in the classification. This
process is done recursively in such a way that a series of gene subsets
and classification models can be obtained in a recursive manner, at
different levels of gene selection. The performance of the
classification can be evaluated either on an independent test data set
or by cross-validation on the same data set. Distributed as Linux
binary.
jSVM
http://www-cad.eecs./~hwawen/research/projects/jsvm/doc/manual/index.html
jSVM
is a Java wrapper for SVMlight.
SvmFu
http://five-percent-nation./SvmFu/
SvmFu,
by Rifkin, is a C++ package for SVM classification. Kernels: linear,
polynomial, and Gaussian radial basis function.
PyML
http://pyml./
PyML
is an interactive object oriented framework for machine learning in
Python. It contains a wrapper for LIBSVM, and procedures for optimizing a
classifier: multi-class methods, descriptor selection, model selection,
jury of classifiers, cross-validation, ROC curves.
BioJava
http://www./
BioJava is an
open-source project dedicated to providing a Java framework for
processing biological data. It include objects for manipulating
sequences, file parsers, DAS client and server suport, access to BioSQL
and Ensembl databases, and powerful analysis and statistical routines
including a dynamic programming toolkit. The package
org.biojava.stats.svm contains SVM classification and regression.