Document Detail

Compact extreme learning machines for biological systems.
MedLine Citation:
PMID:  20852336     Owner:  NLM     Status:  MEDLINE    
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.
Kang Li; Jing Deng; Hai-Bo He; Yurong Li; Da-Jun Du
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Publication Detail:
Type:  Journal Article     Date:  2010-09-16
Journal Detail:
Title:  International journal of computational biology and drug design     Volume:  3     ISSN:  1756-0756     ISO Abbreviation:  Int J Comput Biol Drug Des     Publication Date:  2010  
Date Detail:
Created Date:  2010-09-20     Completed Date:  2010-12-16     Revised Date:  -    
Medline Journal Info:
Nlm Unique ID:  101479540     Medline TA:  Int J Comput Biol Drug Des     Country:  England    
Other Details:
Languages:  eng     Pagination:  112-32     Citation Subset:  IM    
School of Electronics, Electrical Engineering and Computer Science, Queen's University Belfast, Belfast, UK.
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MeSH Terms
Artificial Intelligence
Computer Simulation
Models, Biological*
Neural Networks (Computer)*
Systems Biology / methods*

From MEDLINE®/PubMed®, a database of the U.S. National Library of Medicine

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