This is the SHOGUN machine learning toolbox.

(see INSTALL for first steps on installation and running shogun)
(see README.data for how to download example data sets accompanying shogun)

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, 
Java, C#, Ruby, Lua 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] J. Liu, S. Ji, and J. Ye. SLEP: Sparse Learning with Efficient Projections. Arizona State University, 2009. 
	http://www.public.asu.edu/~jye02/Software/SLEP.

[3] 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.

[4] 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.

[5] 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.

[6] 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.

[7] 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.

[8] 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.

[9] 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.

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

[11] 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.

[12] 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.

[13] 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.

[14] 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.

[15] 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.

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

[17] 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
