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=== Possible Mentors ===

 *Georg Holzmann
 *Thomas Grill

=== Description ===

Support Vector Machines are popular and powerful classifiers/regressors and often used in machine learning problems. In Pd world they could be used for many problems:

 *non-linear mapping of sensor data by learning from examples
 *audio classification and recognition
 *pattern recognition in video data
 *gesture recognition, e.g. together with a Wii controller
 
Currently there is one very popular library for !SVMs, called [http://www.csie.ntu.edu.tw/~cjlin/libsvm/ libsvm] , which is a C++ library for support vector classification (C-SVC, nu-SVC), regression (epsilon-SVR, nu-SVR) and distribution estimation (one-class SVM). In addition, the same authors created [http://www.csie.ntu.edu.tw/~cjlin/liblinear/ liblinear] , which is a linear SVM for large-scale datasets with millions of instances and features.

The goals of this project are:

 *write Pd externals which interface [http://www.csie.ntu.edu.tw/~cjlin/libsvm/ libsvm]  and [http://www.csie.ntu.edu.tw/~cjlin/liblinear/ liblinear]
 *implement some tutorial style examples, which demonstrate for non-experts  how to use these machine learning techniques for (artistic) projects

Resources to start:

 *http://iem.kug.ac.at/pd/externals-HOWTO : a tutorial on how to write   C externals for pure data
 *http://www.csie.ntu.edu.tw/~cjlin/papers/guide/guide.pdf :   a practical guide to SVM classification with libsvm and liblinear
 *http://www.csie.ntu.edu.tw/~cjlin/libsvm/ :   libsvm code
 *http://www.csie.ntu.edu.tw/~cjlin/liblinear/ :   liblinear code


=== Required Skills ===

 *C/C++ programming
 *interest in machine learning and support vector machines


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