Empirical Study of Time Efficiency and Accuracy of Support Vector Machines Using an Improved Version of PSVM
2015 (English)In: Proceedings of the 2015 International Conference on Parallel and Distributed Processing Techniques and Applications: PDPTA 2015: Volume 1 / [ed] Hamid R. Arabnia, Hiroshi Ishii, Kazuki Joe, Hiroaki Nishikawa, Havaru Shouno, Printed in the United States of America: CSREA Press, 2015, Vol. 1, 177-183 p.Conference paper (Refereed)
We present a significantly improved implementation of a parallel SVM algorithm (PSVM) together with a comprehensive experimental study. Support Vector Machines (SVM) is one of the most well-known machine learning classification techniques. PSVM employs the Interior Point Method, which is a solver used for SVM problems that has a high potential of parallelism. We improve PSVM regarding its structure and memory management for contemporary processor architectures. We perform a number of experiments and study the impact of the reduced column size p and other important parameters as C and gamma on the class-prediction accuracy and training time. The experimental results show that there exists a threshold between the number of computational cores and the training time, and that choosing an appropriate value of p effects the choice of the C and gamma parameters as well as the accuracy.
Place, publisher, year, edition, pages
Printed in the United States of America: CSREA Press, 2015. Vol. 1, 177-183 p.
parallel svm, processor technology, training time
Research subject Technology
IdentifiersURN: urn:nbn:se:his:diva-11644ISBN: 1-60132-400-6ISBN: 1-60132-401- 4ISBN: 1 -60132-402-2OAI: oai:DiVA.org:his-11644DiVA: diva2:866010
PDPTA'15 - The 21st International Conference on Parallel and Distributed Processing Techniques and Applications, Las Vegas, July 27-30, 2015