SVM Toolbox
MATLAB Support Vector Machine Toolbox. This is a beta version of a MATLAB toolbox implementing Vapnik’s support vector machine, as described in [1]. Training is performed using the SMO algorithm, due to Platt [2], implemented as a mex file (for speed). Before you use the toolbox you need to run the compilemex script to recompile them (if there are problems running this script, make sure you have the mex compiler set up correctly - you may need to see your sys-admin to do this). 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. For a good introduction to support vector machines, see the excellent book by Cristianini and Shawe-Taylor [3]. Key features of this toolbox: C++ MEX implementation of the SMO training algorithm, with caching of kernel evaluations for efficiency. Support for multi-class support vector classification using max wins, pairwise [4] and DAG-SVM [5] algorithms. A model selection criterion (the xi-alpha bound [6,7] on the leave-one-out cross-validation error). 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.
Keywords for this software
References in zbMATH (referenced in 9 articles )
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Sorted by year (- Durrant, Robert J.; Kabán, Ata: Random projections as regularizers: learning a linear discriminant from fewer observations than dimensions (2015)
- Fagerlund, Seppo: Bird species recognition using support vector machines (2007)
- Bahlmann, Claus: Advanced sequence classification technique applied to online handwriting recognition. (2005)
- van Gestel, Tony; Suykens, Johan A.K.; Baesens, Bart; Viaene, Stijn; Vanthienen, Jan; Dedene, Guido; de Moor, Bart; Vandewalle, Joos: Benchmarking least squares support vector machine classifiers (2004)
- Autio, Ilkka; Elomaa, Tapio: Flexible view recognition for indoor navigation based on Gabor filters and support vector machines. (2003)
- Li, Shutao; Kwok, James T.; Zhu, Hailong; Wang, Yaonan: Texture classification using the support vector machines. (2003)
- Van Gestel, T.; Suykens, J.A.K.; Lanckriet, G.; Lambrechts, A.; De Moor, B.; Vandewalle, J.: Bayesian framework for least-squares support vector machine classifiers, Gaussian processes, and kernel Fisher discriminant analysis (2002)
- Wu, Jiann-Ming: Natural discriminant analysis using interactive Potts models (2002)
- Cawley, Gavin C.; Dorling, Stephen R.; Foxall, Robert J.; Mandic, Danilo P.: Estimating the costs associated with worthwhile predictions of poor air quality (2001)