Projection Support Vector Machine Generators

Authors:
Francisco J. González-Castaño;Ubaldo M. García-Palomares;Robert R. Meyer
Affiliations:
Departamento de Ingeniería Telemática, Universidad de Vigo, ETSI Telecomunicación, Campus, 36200 Vigo, Spain. javier@det.uvigo.es;Dep. de Procesos y Sistemas, Universidad Simón Bolívar, Apartado 89000, Caracas 1080, Venezuela. garciap@usb.ve;Computer Sciences Department, University of Wisconsin-Madison, 1210 West Dayton Street, Madison, WI 53706, USA. rrm@cs.wisc.edu
Venue:
Machine Learning
Year:
2004

Citing 9
Cited 2

Relaxed outer projections, weighted averages and convex feasibility

BIT
The nature of statistical learning theory

The nature of statistical learning theory
On Projection Algorithms for Solving Convex Feasibility Problems

SIAM Review
An introduction to support Vector Machines: and other kernel-based learning methods

An introduction to support Vector Machines: and other kernel-based learning methods
Parallel Optimization: Theory, Algorithms and Applications

Parallel Optimization: Theory, Algorithms and Applications
A Tutorial on Support Vector Machines for Pattern Recognition

Data Mining and Knowledge Discovery
Training Invariant Support Vector Machines

Machine Learning
Lagrangian support vector machines

The Journal of Machine Learning Research
A superlinearly convergent projection algorithm for solving the convex inequality problem

Operations Research Letters

Support Vector Machine Detection of Peer-to-Peer Traffic in High-Performance Routers with Packet Sampling: Nonlinear Kernel Approach

ICCS '07 Proceedings of the 7th international conference on Computational Science, Part III: ICCS 2007
Modeling and optimization of high-technology manufacturing productivity

ISNN'06 Proceedings of the Third international conference on Advances in Neural Networks - Volume Part III

Quantified Score

Hi-index	0.00

Visualization

Abstract

Large-scale Support Vector Machine (SVM) classification is a very active research line in data mining. In recent years, several efficient SVM generation algorithms based on quadratic problems have been proposed, including: Successive OverRelaxation (SOR), Active Support Vector Machines (ASVM) and Lagrangian Support Vector Machines (LSVM). These algorithms have been used to solve classification problems with millions of points. ASVM is perhaps the fastest among them. This paper compares a new projection-based SVM algorithm with ASVM on a selection of real and synthetic data sets. The new algorithm seems competitive in terms of speed and testing accuracy.