Cs229.stanford.edu/notes/cs229-notes3.pdf. Domus Neminis | Support Vector Clustering. SVM Implementation. Hi all; I would like to share a SVM implementation that is accelerated using the GPU that a colleague and I implemented based on prof. Ng's Machine Learning class (not this one, the one that was available from a previous course).
EDIT: We had some Machine Learning algorithms in the AI course and I know this is a bit past the time but I think the demos are quite interesting. I'd appreciate comments and consider yourself invited to join us and develop ML algorithms using the GPU if you'd like to. Demonstration of the algorithm: Explanation about how to use the implementation: Source code: Www.cs.ru.nl/~elenam/esann03.pdf. Research.microsoft.com/pubs/67119/svmtutorial.pdf. Algorithm - Pointers to some good SVM Tutorial. Calypso.inesc-id.pt/docs/SVM1.pdf. Www.cs.cornell.edu/people/tj/publications/finley_joachims_05a.pdf. Www.cs.columbia.edu/~kathy/cs4701/documents/jason_svm_tutorial.pdf.
SVM-Light Support Vector Machine. SVM-Light Support Vector Machine. Overview SVMlight is an implementation of Support Vector Machines (SVMs) in C. The main features of the program are the following: fast optimization algorithm working set selection based on steepest feasible descent "shrinking" heuristic caching of kernel evaluations use of folding in the linear case solves classification and regression problems. Machine Learning Course: If you would like to learn more about Machine Learning, you can find videos, slides, and readings of the course I teach at Cornell here.
SVMstruct: SVM learning for multivariate and structured outputs like trees, sequences, and sets (available here). SVMperf: New training algorithm for linear classification SVMs that can be much faster than SVMlight for large datasets. SVMrank: New algorithm for training Ranking SVMs that is much faster than SVMlight in '-z p' mode. Description The software also provides methods for assessing the generalization performance efficiently. Source Code and Binaries T. Installation Now execute. Supervised Clustering with Support Vector Machines.
Jmlr.csail.mit.edu/papers/volume2/horn01a/rev1/horn01ar1.pdf.