An Analytical Study on Reasoning of Extreme Learning Machine for Classification from Its Inductive Bias

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	An Analytical Study on Reasoning of Extreme Learning Machine for Classification from Its Inductive Bias
	Wong, P. K.; Gao, X.; Wong, K. I.; Vong, C. M.
	2016-07-01
Source Publication	Cognitive Computation (SCI-E)
ISSN	1866-9956
Pages	746-756
Abstract	Since extreme learning machine (ELM) was proposed, hundreds of studies have been conducted on this subject in various areas, from theoretical researches to practical applications. However, there are very few papers in the literature to reveal the reasons why in ELM classification the class with the highest output value is being chosen as the predicted class for a given input. In order to give a clear insight into this question, this paper analyzes the rationality of ELM reasoning from the perspective of its inductive bias. The analysis results show that the choice of highest output in ELM is reasonable for both binary and multiclass classification problems. In addition, to deal with multiclass problems ELM uses the well-known oneagainst- all strategy, in which unclassifiable regions may exist. This paper also gives a clear explanation on how ELM resolves the unclassifiable regions, through both analysis and experiments.
Keyword	Extreme Learning Machine Inductive Bias Reasoning Multiclass
Language	英語English
The Source to Article	PB_Publication
PUB ID	20353
Document Type	Journal article
Collection	Faculty of Science and Technology
Corresponding Author	Wong, P. K.
Recommended Citation GB/T 7714	Wong, P. K.,Gao, X.,Wong, K. I.,et al. An Analytical Study on Reasoning of Extreme Learning Machine for Classification from Its Inductive Bias[J]. Cognitive Computation (SCI-E), 2016, 746-756.
APA	Wong, P. K.., Gao, X.., Wong, K. I.., & Vong, C. M. (2016). An Analytical Study on Reasoning of Extreme Learning Machine for Classification from Its Inductive Bias. Cognitive Computation (SCI-E), 746-756.
MLA	Wong, P. K.,et al."An Analytical Study on Reasoning of Extreme Learning Machine for Classification from Its Inductive Bias".Cognitive Computation (SCI-E) (2016):746-756.

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