title: Compact extreme learning machines for biological systems.
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.
authors.affiliation: School of Electronics, Electrical Engineering and Computer Science, Queen's University Belfast, Belfast, UK. k.li@qub.ac.uk
authors.forename: Kang
authors.initials: K
authors.lastname: Li
authors.forename: Jing
authors.initials: J
authors.lastname: Deng
authors.forename: Hai-Bo
authors.initials: HB
authors.lastname: He
authors.forename: Yurong
authors.initials: Y
authors.lastname: Li
authors.forename: Da-Jun
authors.initials: DJ
authors.lastname: Du
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D003198:Computer Simulation; D006801:Humans; D008954:Models, Biological; D016571:Neural Networks, Computer; D049490:Systems Biology / Q000379:methods*