PUBLICATION DATE

2014-08-01

AUTHORS

S. Balasundaram, Deepak Gupta

TITLE

On optimization based extreme learning machine in primal for regression and classification by functional iterative method

ISSUE

5

VOLUME

7

ISSN (print)

1868-8071

ISSN (electronic)

1868-808X

ABSTRACT

In this paper, the recently proposed extreme learning machine in the aspect of optimization method by Huang et al. (Neurocomputing, 74: 155–163, 2010) has been considered in its primal form whose solution is obtained by solving an absolute value equation problem by a simple, functional iterative algorithm. It has been proved under sufficient conditions that the algorithm converges linearly. The pseudo codes of the algorithm for regression and classification are given and they can be easily implemented in MATLAB. Experiments were performed on a number of real-world datasets using additive and radial basis function hidden nodes. Similar or better generalization performance of the proposed method in comparison to support vector machine (SVM), extreme learning machine (ELM), optimally pruned extreme learning machine (OP-ELM) and optimization based extreme learning machine (OB-ELM) methods with faster learning speed than SVM and OB-ELM demonstrates its effectiveness and usefulness.

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34 TRIPLES      30 PREDICATES      35 URIs      22 LITERALS

Subject Predicate Object
1 articles:57975780a99d2e05a7bcb3dbe150c8ed sg:abstract Abstract In this paper, the recently proposed extreme learning machine in the aspect of optimization method by Huang et al. (Neurocomputing, 74: 155–163, 2010) has been considered in its primal form whose solution is obtained by solving an absolute value equation problem by a simple, functional iterative algorithm. It has been proved under sufficient conditions that the algorithm converges linearly. The pseudo codes of the algorithm for regression and classification are given and they can be easily implemented in MATLAB. Experiments were performed on a number of real-world datasets using additive and radial basis function hidden nodes. Similar or better generalization performance of the proposed method in comparison to support vector machine (SVM), extreme learning machine (ELM), optimally pruned extreme learning machine (OP-ELM) and optimization based extreme learning machine (OB-ELM) methods with faster learning speed than SVM and OB-ELM demonstrates its effectiveness and usefulness.
2 sg:articleType OriginalPaper
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5 sg:ddsId s13042-014-0283-8
6 sg:ddsIdJournalBrand 13042
7 sg:doi 10.1007/s13042-014-0283-8
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19 sg:issnElectronic 1868-808X
20 sg:issnPrint 1868-8071
21 sg:issue 5
22 sg:language English
23 sg:license http://scigraph.springernature.com/explorer/license/
24 sg:pageEnd 728
25 sg:pageStart 707
26 sg:publicationDate 2014-08-01
27 sg:publicationYear 2014
28 sg:publicationYearMonth 2014-08
29 sg:scigraphId 57975780a99d2e05a7bcb3dbe150c8ed
30 sg:title On optimization based extreme learning machine in primal for regression and classification by functional iterative method
31 sg:volume 7
32 sg:webpage https://link.springer.com/10.1007/s13042-014-0283-8
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34 rdfs:label Article: On optimization based extreme learning machine in primal for regression and classification by functional iterative method
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