Kernel-based online machine learning and support vector reduction

Authors:
Sumeet Agarwal;V. Vijaya Saradhi;Harish Karnick
Affiliations:
Systems Biology Doctoral Training Centre, University of Oxford, UK;Department of Computer Science and Engineering, Indian Institute of Technology Kanpur, Kanpur, India;Department of Computer Science and Engineering, Indian Institute of Technology Kanpur, Kanpur, India
Venue:
Neurocomputing
Year:
2008

Citing 14
Cited 3

The nature of statistical learning theory

The nature of statistical learning theory
Fast training of support vector machines using sequential minimal optimization

Advances in kernel methods
A Simple Decomposition Method for Support Vector Machines

Machine Learning
Sparse bayesian learning and the relevance vector machine

The Journal of Machine Learning Research
Efficient svm training using low-rank kernel representations

The Journal of Machine Learning Research
Exact simplification of support vector solutions

The Journal of Machine Learning Research
Ultraconservative online algorithms for multiclass problems

The Journal of Machine Learning Research
Sparseness of support vector machines

The Journal of Machine Learning Research
An efficient method for simplifying support vector machines

ICML '05 Proceedings of the 22nd international conference on Machine learning
Building Sparse Large Margin Classifiers

ICML '05 Proceedings of the 22nd international conference on Machine learning
Bounds on Error Expectation for Support Vector Machines

Neural Computation
Building Support Vector Machines with Reduced Classifier Complexity

The Journal of Machine Learning Research
Online learning with kernels

IEEE Transactions on Signal Processing
A fast iterative nearest point algorithm for support vector machine classifier design

IEEE Transactions on Neural Networks

An online core vector machine with adaptive MEB adjustment

Pattern Recognition
Forecasting of turbine heat rate with online least squares support vector machine based on gravitational search algorithm

Knowledge-Based Systems
Application of an incremental SVM algorithm for on-line human recognition from video surveillance using texture and color features

Neurocomputing

Quantified Score

Hi-index	0.03

Visualization

Abstract

We apply kernel-based machine learning methods to online learning situations, and look at the related requirement of reducing the complexity of the learnt classifier. Online methods are particularly useful in situations which involve streaming data, such as medical or financial applications. We show that the concept of span of support vectors can be used to build a classifier that performs reasonably well while satisfying given space and time constraints, thus making it potentially suitable for such online situations. The span-based heuristic is observed to be effective under stringent memory limits (that is when the number of support vectors a machine can hold is very small).