Title

Compact extreme learning machines for biological systems

Document Type

Article

Date of Original Version

1-1-2010

Abstract

In biological system modelling using data-driven black-box methods, it is essential to effectively and efficiently produce a parsimonious model to represent the system behaviour. The Extreme Learning Machine (ELM) is a recent development in fast learning paradigms. However, the derived model is not necessarily sparse. In this paper, an improved ELM is investigated, aiming to obtain a more compact model without significantly increasing the overall computational complexity. This is achieved by associating each model term to a regularized parameter, thus insignificant ones are automatically unselected, leading to improved model sparsity. Experimental results on biochemical data confirm its effectiveness. © 2010 Inderscience Enterprises Ltd.

Publication Title

International Journal of Computational Biology and Drug Design

Volume

3

Issue

2

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