支持向量机(matlab源码)

MATLAB Support Vector Machine Toolbox ===================================== (c) Dr Gavin Cawley, September 2000. This is a (slightly less) beta version of a MATLAB toolbox implementing Vapnik's support vector machine, as described in [1]. The toolbox currently supports multi-class pattern recognition, Platt's sequential minimal optimisation algorithm [2] and an efficient estimate of the leave-oe-out cross-validation error [3]. The SMO training algorithm is implemented as a mex file (for speed), and a .mexlx file for Linux machines is supplied. At the moment this is the only documentation for the toolbox but the file demo.m provides a simple demonstration that ought to be enough to get started. Key features: (a) C++ MEX implementation of the SMO training algorithm, with caching of kernel evaluations for efficiency. (b) Support for multi-class pattern recognition. (c) An efficient criterion for model selection. (d) Object oriented design, currently this just means that you can supply bespoke kernel functions for particular applications, but will in future releases also support a range of training algorithms, model selection criteria etc. LICENSING ARRANGEMENTS ====================== The toolbox is provided free for non-commercial use under the terms of the GNU GPL licence (see licence.txt in this directory), however, I would be grateful if: (a) you let me know about any bugs you find, (b) you send suggestions of ideas to improve the toolbox (e.g. references to other training algorithms), (c) reference the toolbox web page in any publication describing research performed using the toolbox, or software derived from the toolbox. A suitable BibTeX entry would look something like this: @misc{Cawley2000, author = "Cawley, G. C.", title = "{MATLAB} Support Vector Machine Toolbox (v0.50$\beta$) $[$ \texttt{http://theoval.sys.uea.ac.uk/\~{}gcc/svm/toolbox}$]$", howpublished = "University of East Anglia, School of Information Systems, Norwich, Norfolk, U.K. NR4 7TJ", year = 2000 } TO DO LIST ========== 1. Find time to write a proper list of things to do! 2. Documentation. 3. Support Vector Regression. 4. Automated model selection. REFERENCES ========== [1] V.N. Vapnik, "The Nature of Statistical Learning Theory", Springer-Verlag, New York, ISBN 0-387-94559-8, 1995. [2] J. C. Platt, "Fast training of support vector machines using sequential minimal optimization", in Advances in Kernel Methods - Support Vector Learning, (Eds) B. Scholkopf, C. Burges, and A. J. Smola, MIT Press, Cambridge, Massachusetts, chapter 12, pp 185-208, 1999. [3] T. Joachims, "Estimating the Generalization Performance of a SVM Efficiently", LS-8 Report 25, Universitat Dortmund, Fachbereich Informatik, 1999.