Compact extreme learning machines for biological systems

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...

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Veröffentlicht in:International journal of computational biology and drug design Jg. 3; H. 2; S. 112
Hauptverfasser: Li, Kang, Deng, Jing, He, Hai-Bo, Li, Yurong, Du, Da-Jun
Format: Journal Article
Sprache:Englisch
Veröffentlicht: England 2010
Schlagworte:
Algorithms
Artificial Intelligence
Computer Simulation
Humans
Models, Biological
Neural Networks (Computer)
Systems Biology - methods
ISSN:1756-0756
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Zusammenfassung: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.
Bibliographie:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
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ISSN:1756-0756
DOI:10.1504/IJCBDD.2010.035238