A training algorithm for optimal margin classifiers
COLT '92 Proceedings of the fifth annual workshop on Computational learning theory
Fast training of support vector machines using sequential minimal optimization
Advances in kernel methods
Text Categorization with Suport Vector Machines: Learning with Many Relevant Features
ECML '98 Proceedings of the 10th European Conference on Machine Learning
Training Support Vector Machines: an Application to Face Detection
CVPR '97 Proceedings of the 1997 Conference on Computer Vision and Pattern Recognition (CVPR '97)
Improvements to Platt's SMO Algorithm for SVM Classifier Design
Neural Computation
Working Set Selection Using Second Order Information for Training Support Vector Machines
The Journal of Machine Learning Research
Fast support vector machine training and classification on graphics processors
Proceedings of the 25th international conference on Machine learning
From Ensemble of Fuzzy Classifiers to Single Fuzzy Rule Base Classifier
ICAISC '08 Proceedings of the 9th international conference on Artificial Intelligence and Soft Computing
Parallel multiclass classification using SVMs on GPUs
Proceedings of the 3rd Workshop on General-Purpose Computation on Graphics Processing Units
Support Vector Machines on GPU with Sparse Matrix Format
ICMLA '10 Proceedings of the 2010 Ninth International Conference on Machine Learning and Applications
LIBSVM: A library for support vector machines
ACM Transactions on Intelligent Systems and Technology (TIST)
Support vector machines for histogram-based image classification
IEEE Transactions on Neural Networks
| Hi-index | 0.00 |
The SVM algorithm is one of the most frequently used methods for the classification process. For many domains, where the classification problems have many features as well as numerous instances, classification is a difficult and time-consuming task. For this reason, the following paper presents the CSR-GPU-SVM algorithm which accelerates SVM training for large and sparse problems with the use of the CUDA technology. Implementation is based on the SMO (Sequential Minimal Optimization) algorithm and utilizes the CSR(Compressed Sparse Row) sparse matrix format. The proposed solution allows us to perform efficient classification of big datasets, for example rcv1 and newsgroup20, for which classification with dense representation is not possible. The performed experiments have proven the accelerations in the order of 6 - 35 training times compared to original LibSVM implementation.