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This is the SHOGUN machine learning toolbox.

INTRODUCTION

The machine learning toolbox's focus is on large scale kernel methods and
especially on Support Vector Machines (SVM)[1]. It provides a generic SVM
object interfacing to several different SVM implementations, among them the
state of the art LibSVM[2] and SVMlight[3].  Each of the SVMs can be
combined with a variety of kernels. The toolbox not only provides efficient
implementations of the most common kernels, like the Linear, Polynomial,
Gaussian and Sigmoid Kernel but also comes with a number of recent string
kernels as e.g. the Locality Improved[4], Fischer[5], TOP[6], Spectrum[7],
Weighted Degree Kernel (with shifts)[8][9][10]. For the latter the efficient
LINADD[10] optimizations are implemented.  Also SHOGUN offers the freedom of
working with custom pre-computed kernels.  One of its key features is the
``combined kernel'' which can be constructed by a weighted linear combination
of a number of sub-kernels, each of which not necessarily working on the same
domain. An optimal sub-kernel weighting can be learned using Multiple Kernel
Learning[11][12][16].
Currently SVM 2-class classification and regression problems can be dealt
with. However SHOGUN also implements a number of linear methods like Linear
Discriminant Analysis (LDA), Linear Programming Machine (LPM), (Kernel)
Perceptrons and features algorithms to train hidden markov models.
The input feature-objects can be dense, sparse or strings and
of type int/short/double/char and can be converted into different feature types.
Chains of ``preprocessors'' (e.g. substracting the mean) can be attached to
each feature object allowing for on-the-fly pre-processing.

INTERFACES

SHOGUN is implemented in C++ and interfaces to Matlab(tm), R, Octave and
Python.

PLATFORMS

Debian GNU/Linux, Mac OSX and WIN32/CYGWIN are supported platforms (see
the INSTALL file for generic and platform specific installation instructions)

APPLICATIONS

We have successfully used this toolbox to tackle the following sequence
analysis problems: Protein Super Family classification[6],
Splice Site Prediction[8][13][14], Interpreting the SVM Classifier[11,12],
Splice Form Prediction[8], Alternative Splicing[9] and Promotor
Prediction[15]. Some of them come with no less than 10
million training examples, others with 7 billion test examples.

LICENSE

Except for the files classifier/svm/Optimizer.{cpp,h},
classifier/svm/SVM_light.{cpp,h}, regression/svr/SVR_light.{cpp,h}
and the kernel caching functions in kernel/Kernel.{cpp,h}
which are (C) Torsten Joachims and follow a different
licensing scheme (cf. LICENSE.SVMLight) SHOGUN is licensed under the GPL
version 3 or any later version (cf. LICENSE).

AVAILABILITY

SHOGUN can be downloaded at
	http://www.shogun-toolbox.org

REFERENCES

[1] C.~Cortes and V.N. Vapnik.  Support-vector networks.
	Machine Learning, 20(3):273--297, 1995.

[2] C.-C. Chang and C.-J. Lin.  Libsvm: Introduction and benchmarks.
	Technical report, Department of Computer Science and Information
	Engineering, National Taiwan University, Taipei, 2000.

[3] T.Joachims. Making large-scale SVM learning practical. In B.~Schoelkopf,
	C.J.C. Burges, and A.J. Smola, editors, Advances in Kernel Methods -
	Support Vector Learning, pages 169--184, Cambridge, MA, 1999. MIT Press.

[4] A.Zien, G.Raetsch, S.Mika, B.Schoelkopf, T.Lengauer, and K.-R.
	Mueller. Engineering Support Vector Machine Kernels That Recognize
	Translation Initiation Sites. Bioinformatics, 16(9):799-807, September 2000.

[5] T.S. Jaakkola and D.Haussler.Exploiting generative models in
	discriminative classifiers. In M.S. Kearns, S.A. Solla, and D.A. Cohn,
	editors, Advances in Neural Information Processing Systems, volume 11,
	pages 487-493, 1999.

[6] K.Tsuda, M.Kawanabe, G.Raetsch, S.Sonnenburg, and K.R. Mueller.
	A new discriminative kernel from probabilistic models.
	Neural Computation, 14:2397--2414, 2002.

[7] C.Leslie, E.Eskin, and W.S. Noble. The spectrum kernel: A string kernel
	for SVM protein classification. In R.B. Altman, A.K. Dunker, L.Hunter,
	K.Lauderdale, and T.E. Klein, editors, Proceedings of the Pacific
	Symposium on Biocomputing, pages 564-575, Kaua'i, Hawaii, 2002.

[8] G.Raetsch and S.Sonnenburg. Accurate Splice Site Prediction for
	Caenorhabditis Elegans, pages 277-298. MIT Press series on Computational
	Molecular Biology. MIT Press, 2004.

[9] G.Raetsch, S.Sonnenburg, and B.Schoelkopf. RASE: recognition of
	alternatively spliced exons in c. elegans. Bioinformatics,
	21:i369--i377, June 2005.

[10] S.Sonnenburg, G.Raetsch, and B.Schoelkopf. Large scale genomic sequence
	SVM classifiers. In Proceedings of the 22nd International Machine Learning
	Conference. ACM Press, 2005.

[11] S.Sonnenburg, G.Raetsch, and C.Schaefer. Learning interpretable SVMs
	for biological sequence classification. In RECOMB 2005, LNBI 3500,
	pages 389-407. Springer-Verlag Berlin Heidelberg, 2005.

[12] G.Raetsch, S.Sonnenburg, and C.Schaefer. Learning Interpretable SVMs
	for Biological Sequence Classification. BMC Bioinformatics, Special Issue
	from NIPS workshop on New Problems and Methods in Computational Biology
	Whistler, Canada, 18 December 2004, 7:(Suppl. 1):S9, March 2006.

[13] S.Sonnenburg.New methods for splice site recognition. Master's thesis,
	Humboldt University, 2002. supervised by K.-R. Mueller H.-D. Burkhard and
	G.Raetsch.

[14] S.Sonnenburg, G.Raetsch, A.Jagota, and K.-R. Mueller. New methods for
	splice-site recognition. In Proceedings of the International Conference on
	Artifical Neural Networks, 2002.  Copyright by Springer.

[15] S.Sonnenburg, A.Zien, and G.Raetsch. ARTS: Accurate Recognition of
	Transcription Starts in Human. 2006.  (accepted).

[16] S.Sonnenburg, G.Raetsch, C.Schaefer, and B.Schoelkopf,Large Scale
	Multiple Kernel Learning, Journal of Machine Learning Research, 2006,
	K.Bennett and E.P.-Hernandez Editors, (accepted)