Compact Extreme Learning Machines for biological systems
by Kang Li, Jing Deng, Hai-Bo He, Da-Jun Du
International Journal of Computational Biology and Drug Design (IJCBDD), Vol. 3, No. 2, 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.

Online publication date: Thu, 16-Sep-2010

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