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常用支持向量机（SVM）软件
(2007-08-04 00:37:51)
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IT/科技svmlightherosvmlibsvmmysvmspidersvmstructwekajsvm |
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, 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, 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, a Java version of mySVM is part of the YaLE (Yet Another
Learning Environment) learning environment.
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 (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, 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, 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, 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 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.
This SVM implementation in R (http://www.r-project.org/) contains
C-classification, n-classification, e-regression, and n-regression.
Kernels: linear, polynomial, radial basis, neural
(tanh).
Multi-class SVM implementation in C by Guermeur.
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.sdsc.edu and
it is a very convenient option for users that want to experiment
with small datasets (several hundreds patterns). Kernels: linear,
polynomial, radial.
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 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 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, by Chakrabartty and Cauwenberghs, is a multi-class
probability regression engine that generates conditional
probability distribution as a solution. Available as source
code.
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, 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 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.
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.
MATLAB scripts for the book Learning and Soft Computing by Kecman,
implementing SVM classification and regression.
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, 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.
This is a MATLAB SVM classification implementation which can handle
1-norm and 2-norm SVM (linear or quadratic loss
functions).
SVM/LOO, by Cauwenberghs, has a very efficient MATLAB
implementation of the leave-one-out cross-validation.
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 (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 (Active Support Vector Machine) is a very fast linear SVM
script for MATLAB, by Musicant and Mangasarian, developed for large
datasets.
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.
This MATLAB toolbox is based on LIBSVM.
SimpleSVM Toolbox is a MATLAB implementation of the SimpleSVM
algorithm.
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.
Developed by Cawley, has standard SVM features, together with
multi-class classification and leave-one-out
cross-validation.
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 is a Java wrapper for SVMlight.
SvmFu, by Rifkin, is a C++ package for SVM classification. Kernels:
linear, polynomial, and Gaussian radial basis function.
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 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.
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