Communications of the ACM - Special issue on parallelism
The nature of statistical learning theory
The nature of statistical learning theory
A Database for Handwritten Text Recognition Research
IEEE Transactions on Pattern Analysis and Machine Intelligence
In Defense of One-Vs-All Classification
The Journal of Machine Learning Research
Using GPUs for Machine Learning Algorithms
ICDAR '05 Proceedings of the Eighth International Conference on Document Analysis and Recognition
Improvements to Platt's SMO Algorithm for SVM Classifier Design
Neural Computation
Training linear SVMs in linear time
Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
Working Set Selection Using Second Order Information for Training Support Vector Machines
The Journal of Machine Learning Research
Mapping computational concepts to GPUs
SIGGRAPH '05 ACM SIGGRAPH 2005 Courses
Parallel Software for Training Large Scale Support Vector Machines on Multiprocessor Systems
The Journal of Machine Learning Research
A Novel Model of Working Set Selection for SMO Decomposition Methods
ICTAI '07 Proceedings of the 19th IEEE International Conference on Tools with Artificial Intelligence - Volume 02
Scalable Parallel Programming with CUDA
Queue - GPU Computing
Fast support vector machine training and classification on graphics processors
Proceedings of the 25th international conference on Machine learning
A comparison of methods for multiclass support vector machines
IEEE Transactions on Neural Networks
Parallel sequential minimal optimization for the training of support vector machines
IEEE Transactions on Neural Networks
Distributed Parallel Support Vector Machines in Strongly Connected Networks
IEEE Transactions on Neural Networks
SVM with CUDA accelerated kernels for big sparse problems
ICAISC'12 Proceedings of the 11th international conference on Artificial Intelligence and Soft Computing - Volume Part I
Parallel multitask cross validation for Support Vector Machine using GPU
Journal of Parallel and Distributed Computing
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The scaling of serial algorithms cannot rely on the improvement of CPUs anymore. The performance of classical Support Vector Machine (SVM) implementations has reached its limit and the arrival of the multi core era requires these algorithms to adapt to a new parallel scenario. Graphics Processing Units (GPU) have arisen as high performance platforms to implement data parallel algorithms. In this paper, it is described how a naïve implementation of a multiclass classifier based on SVMs can map its inherent degrees of parallelism to the GPU programming model and efficiently use its computational throughput. Empirical results show that the training and classification time of the algorithm can be reduced an order of magnitude compared to a classical multiclass solver, LIBSVM, while guaranteeing the same accuracy.