Kernel-based online machine learning and support vector reduction

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). r 2008 Elsevier B.V. All rights reserved.

• Type: Conference
• Year: 2007
• Where: ESANN
• Authors: Sumeet Agarwal, V. Vijaya Saradhi, Harish Karnick
• Keywords: ESANN 2007, Machine Learning Methods, Neural Networks, Stringent Memory Limits, Support Vectors