Merge pull request #2984 from iglesias/develop

Revert "added license files" (bringing README.md back to life)

README.md

@@ -1 +1,213 @@
-# ModernAnalytics
+# The SHOGUN machine learning toolbox
+-------------------------------------
+
+Develop branch build status:
+
+[![Build Status](https://travis-ci.org/shogun-toolbox/shogun.png?branch=develop)](https://travis-ci.org/shogun-toolbox/shogun)
+[![Coverage Status](https://coveralls.io/repos/shogun-toolbox/shogun/badge.png?branch=develop)](https://coveralls.io/r/shogun-toolbox/shogun?branch=develop)
+
+Buildbot: http://buildbot.shogun-toolbox.org/waterfall.
+
+Quick links to this file:
+
+* [Quickstart](doc/md/QUICKSTART.md)
+* [Introduction](#introduction)
+* [Interfaces](#interfaces)
+* [Platforms](#platforms)
+* [Contents](#contents)
+* [Applications](#applications)
+* [License](#license)
+* [Download](#download)
+* [References](#references)
+
+Other links that may be useful:
+
+* See [INSTALL](doc/md/INSTALL.md) for first steps on installation and running SHOGUN.
+* See [README.developer](doc/md/README_developer.md) for the developer documentation.
+* See [README.data](doc/md/README_data.md) for how to download example data sets accompanying SHOGUN.
+* See [README.cmake](doc/md/README_cmake.md) for setting particular build options with SHOGUN and cmake.
+
+## 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 types int/short/double/char. In addition, they 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.
+
+Shogun got initiated by Soeren Sonnenburg and Gunnar Raetsch (thats where the
+name ShoGun originates from). It is now developed by a much larger Team
+cf. [AUTHORS](doc/md/AUTHORS.md) and would not have been possible without the patches
+and bug reports by various people and by the various authors of other machine
+learning packages that we utilize. See [CONTRIBUTIONS](doc/md/CONTRIBUTIONS.md) for
+a detailled list.
+
+## Interfaces
+-------------
+
+SHOGUN is implemented in C++ and interfaces to Matlab(tm), R, Octave,
+Java, C#, Ruby, Lua and Python.
+
+The following table depicts the status of each interface available in SHOGUN:
+
+|    Interface     |     Status                                                |
+|:----------------:|-----------------------------------------------------------|
+|python\_modular   | *mature* (no known problems)                              |
+|octave\_modular   | *mature* (no known problems)                              |
+|java\_modular     | *stable* (no known problems; not all examples are ported) |
+|ruby\_modular     | *stable* (no known problems; only few examples ported)    |
+|csharp\_modular   | *stable* (no known problems; not all examples are ported) |
+|lua\_modular      | *alpha* (some examples work, string typemaps are unstable |
+|perl\_modular     | *pre-alpha* (work in progress quality)                    |
+|r\_modular        | *pre-alpha* (SWIG does not properly handle reference counting and thus only for the brave, <br/> --disable-reference-counting to get it to work, but beware that it will leak memory; disabled by default)           |
+|octave\_static    | *mature* (no known problems)                                |
+|matlab\_static    | *mature* (no known problems)                                |
+|python\_static    | *mature* (no known problems)                                |
+|r\_static         | *mature* (no known problems)                                |
+|libshogun\_static | *mature* (no known problems)                                |
+|cmdline\_static   | *stable* (but some data types incomplete)                 |
+|elwms\_static     | this is the eierlegendewollmilchsau interface, a chimera that in one file interfaces with python, octave, r, matlab and provides the run\_python command to run code in python using the in octave,r,matlab available variables, etc)    |
+
+Visit http://www.shogun-toolbox.org/doc/en/current for further information.
+
+
+## Platforms
+------------
+
+Debian GNU/Linux, Mac OSX and WIN32/CYGWIN are supported platforms (see
+the [INSTALL](doc/md/INSTALL.md) file for generic and platform specific installation instructions).
+
+## Contents
+-----------
+
+The following directories are found in the source distribution.
+
+- *src* - source code.
+- *data* - data sets (required for some examples / applications - these need to be downloaded
+    separately via the download site or `git submodule update --init` from the root of the git checkout.
+- *doc* - documentation (to be built using doxygen), ipython notebooks, and the PDF tutorial.
+- *examples* - example files for all interfaces.
+- *applications* - applications of SHOGUN.
+- *benchmarks* - speed benchmarks.
+- *tests* - unit and integration tests.
+- *cmake* - cmake build scripts
+
+## 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](doc/md/LICENSE_SVMlight.md)) SHOGUN is
+generally licensed under the GPL version 3 or any later version (cf.
+[LICENSE](doc/md/LICENSE.md)) with code borrowed from various GPL compatible
+libraries from various places (cf. [CONTRIBUTIONS](doc/md/CONTRIBUTIONS.md)). See also
+[LICENSE\_msufsort](doc/md/LICENSE_msufsort.md) and  [LICENSE\_tapkee](doc/md/LICENSE_tapkee.md).
+
+## Download
+-----------
+
+SHOGUN can be downloaded from http://www.shogun-toolbox.org and GitHub at
+https://github.com/shogun-toolbox/shogun.
+
+## 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.
+
+[![githalytics.com alpha](https://cruel-carlota.pagodabox.com/3e5ff04ff56513867eedb5b2f4261702 "githalytics.com")](http://githalytics.com/shogun-toolbox/shogun)