Making large-scale support vector machine learning practical
Advances in kernel methods
Fast training of support vector machines using sequential minimal optimization
Advances in kernel methods
Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond
Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond
Training Support Vector Machines: an Application to Face Detection
CVPR '97 Proceedings of the 1997 Conference on Computer Vision and Pattern Recognition (CVPR '97)
On the algorithmic implementation of multiclass kernel-based vector machines
The Journal of Machine Learning Research
Fast Kernel Classifiers with Online and Active Learning
The Journal of Machine Learning Research
Working Set Selection Using Second Order Information for Training Support Vector Machines
The Journal of Machine Learning Research
Pegasos: Primal Estimated sub-GrAdient SOlver for SVM
Proceedings of the 24th international conference on Machine learning
Fast support vector machine training and classification on graphics processors
Proceedings of the 25th international conference on Machine learning
A dual coordinate descent method for large-scale linear SVM
Proceedings of the 25th international conference on Machine learning
Speed Up SVM Algorithm for Massive Classification Tasks
ADMA '08 Proceedings of the 4th international conference on Advanced Data Mining and Applications
LIBSVM: A library for support vector machines
ACM Transactions on Intelligent Systems and Technology (TIST)
Pegasos: primal estimated sub-gradient solver for SVM
Mathematical Programming: Series A and B - Special Issue on "Optimization and Machine learning"; Alexandre d’Aspremont • Francis Bach • Inderjit S. Dhillon • Bin Yu
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We present a method for efficiently training binary and multiclass kernelized SVMs on a Graphics Processing Unit (GPU). Our methods apply to a broad range of kernels, including the popular Gaus- sian kernel, on datasets as large as the amount of available memory on the graphics card. Our approach is distinguished from earlier work in that it cleanly and efficiently handles sparse datasets through the use of a novel clustering technique. Our optimization algorithm is also specifically designed to take advantage of the graphics hardware. This leads to different algorithmic choices then those preferred in serial implementations. Our easy-to-use library is orders of magnitude faster then existing CPU libraries, and several times faster than prior GPU approaches.