Multi-threaded support vector machines for pattern recognition

Authors:
João Gonçalves;Noel Lopes;Bernardete Ribeiro
Affiliations:
CISUC, Department of Informatics Engineering, University of Coimbra, Portugal;CISUC, Department of Informatics Engineering, University of Coimbra, Portugal,UDI/IPG - Research Unit, Polytechnic Institute of Guarda, Portugal;CISUC, Department of Informatics Engineering, University of Coimbra, Portugal
Venue:
ICONIP'12 Proceedings of the 19th international conference on Neural Information Processing - Volume Part II
Year:
2012

Citing 12
Cited 0

The nature of statistical learning theory

The nature of statistical learning theory
Support-Vector Networks

Machine Learning
KMOD " A Tw o-Parameter SVM Kernel for Pattern Recognition

ICPR '02 Proceedings of the 16 th International Conference on Pattern Recognition (ICPR'02) Volume 3 - Volume 3
Improvements to Platt's SMO Algorithm for SVM Classifier Design

Neural Computation
A Framework for Learning Predictive Structures from Multiple Tasks and Unlabeled Data

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
Feature Mining and Intelligent Computing for MP3 Steganalysis

IJCBS '09 Proceedings of the 2009 International Joint Conference on Bioinformatics, Systems Biology and Intelligent Computing
Beyond business failure prediction

Expert Systems with Applications: An International Journal
Subset based least squares subspace regression in RKHS

Neurocomputing
Optimized fixed-size kernel models for large data sets

Computational Statistics & Data Analysis
An incremental hypersphere learning framework for protein membership prediction

HAIS'12 Proceedings of the 7th international conference on Hybrid Artificial Intelligent Systems - Volume Part I
Parallel sequential minimal optimization for the training of support vector machines

IEEE Transactions on Neural Networks

Quantified Score

Hi-index	0.00

Visualization

Abstract

Support Vector Machines (SVM) have become indispensable tools in the area of pattern recognition. They show powerful classification and regression performance in highly non-linear problems by mapping the input vectors nonlinearly into a high-dimensional feature space through a kernel function. However, the optimization task is numerically expensive since single-threaded implementations are hardly able to cope up with the complex learning task. In this paper, we present a multi-threaded implementation of the Sequential Minimal Optimization (SMO) which reduces the numerical complexity by parallelizing the KKT conditions update, the calculation of the hyperplane offset and the classification task. Our preliminary results both in benchmark datasets and real-world problems show competitive performance to the state-of-the-art tools while the execution running times are considerably faster.