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International Journal of Machine Learning and Cybernetics
Erschienen in: International Journal of Machine Learning and Cybernetics 5/2016

01.10.2016 | Original Article

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

verfasst von: S. Balasundaram, Deepak Gupta

Erschienen in: International Journal of Machine Learning and Cybernetics | Ausgabe 5/2016

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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.
Vorheriger Artikel Robust stabilization for uncertain T–S fuzzy singular system
Nächster Artikel Multiple recursive projection twin support vector machine for multi-class classification

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Literatur
1.
Balasundaram S, Gupta D, Kapil (2014) 1-norm extreme learning machine for regression and multiclass classification using Newton method. Neuocomputing 128:4–14CrossRef
2.
Balasundaram S, Kapil (2013) On extreme learning machine for ε–insensitive regression in the primal by Newton method. Neural Comput Appl 22:559–567CrossRef
3.
Balasundaram S, Kapil (2011) Application of error minimized extreme learning machine for simultaneous learning of a function and its derivatives. Neurocomputing 74:2511–2519CrossRef
4.
Binu PC, Vimal Krishnan VR, Raju G, Babu Anto P (2012) Handwritten character recognition using wavelet energy and extreme learning machine. Int J Mach Learn Cybern 3(2):149–161CrossRef
5.
Box GEP, Jenkins GM (1976) Time series analysis: forecasting and Control. Holden-Day, San FranciscoMATH
6.
Chen Z, Zhu H, Wang Y (2013) A modified extreme learning machine with sigmoidal activation functions. Neural Comput Appl 22:541–550MathSciNetCrossRef
7.
Cristianini N, Shawe-Taylor J (2000) An introduction to support vector machines and other kernel based learning methods. Cambridge University Press, CambridgeCrossRefMATH
8.
9.
Demsar J (2006) Statistical comparisons of classifiers over multiple data sets. J Mach Learn Res 7: 1-30
10.
Deng W, Chen L (2010) Color image watermarking using regularized extreme learning machine. Neural Netw World 20(3):317–330
11.
Gretton A, Doucet A, Herbrich R, Rayner PJW, Scholkopf B (2001) Support vector regression for black-box system identification, In: Proceedings of the 11th IEEE Workshop on Statistical Signal Processing
12.
Feng G, Huang G-B, Lin Q, Gay R (2009) Error minimized extreme learning machine with growth of hidden nodes and incremental learning. IEEE Trans Neural Netw 20(8):1352–1357CrossRef
13.
Frenay B, Verleysen M (2010) Using SVMs with randomized feature spaces: an extreme learning approach. In: Proceedings of the 18th European Symposium on Artificial Neural Networks (ESANN), Bruges, Belgium, pp. 315–320
14.
15.
Huang G-B, Chen L (2007) Convex incremental extreme learning machine. Neurocomputing 70:3056–3062CrossRef
16.
Huang G-B, Chen L (2008) Enhanced random search based incremental extreme learning machine. Neurocomputing 71:3460–3468CrossRef
17.
Huang G-B, Chen L, Siew C-K (2006) Universal approximation using incremental constructive feedforward networks with random hidden nodes. IEEE Trans Neural Netw 17(4):879–892CrossRef
18.
Huang G-B, Ding X, Zhou H (2010) Optimization method based extreme learning machine for classification. Neurocomputing 74:155–163CrossRef
19.
Huang G-B, Wang DH, Lan Y (2011) Extreme learning machines: a survey. Int J Mach Learn Cybern 2:107–122CrossRef
20.
Huang G-B, Zhou H, Ding X, Zhang R (2012) Extreme learning machine for regression and multiclass classification. IEEE Trans Syst, Man Cybern Part B Cybern 42(2):513–528CrossRef
21.
Huang G-B, Zhu Q-Y, Siew C-K (2006) Extreme learning machine: theory and applications. Neurocomputing 70:489–501CrossRef
22.
Jun W, Shitong W, Chung F (2011) Positive and negative fuzzy rule system, extreme learning machine and image classification. Int J Mach Learn Cybern 2(4):261–271CrossRef
23.
Lan Y, Soh C, Huang G-B (2010) Two-stage extreme learning machine regression. Neurocomputing 73:3028–3038CrossRef
24.
Liu Q, He Q, Shi Z (2008) Extreme support vector machine classifier. LNCS 5012:222–233
25.
26.
Miche Y, Sorjamaa A, Bas P, Simula O, Jutten C, Lendasse A (2010) OP-ELM: optimally pruned extreme learning machine. IEEE Trans Neural Netw 21(1):158–162CrossRef
27.
Minhas R, Baradarani A, Seifzadeh S, Wu QMJ (2010) Human action recognition using extreme learning machine based on visual vocabularies. Neurocomputing 73:1906–1917CrossRef
28.
Mohammed AA, Wu QMJ, Sid-Ahmed MA (2010) Application of wave atoms decomposition and extreme learning machine for finger print classification. LNCS 6112:246–256
29.
Mukherjee S, Osuna E, Girosi F (1997) Nonlinear prediction of chaotic time series using support vector machines, in: NNSP’97: Neural Networks for Signal Processing VII: in Proceedigs of IEEE Signal Processing Society Workshop, Amelia Island, FL, USA, pp. 511–520
30.
Muller KR, Smola AJ, Ratsch G, Schölkopf B, Kohlmorgen J (1999) Using support vector machines for time series prediction. In: Schölkopf B, Burges CJC, Smola AJ (eds) Advances in kernel methods—support vector learning. MIT Press, Cambridge, pp 243–254
31.
32.
Rao CR, Mitra SK (1971) Generalized inverse of matrices and its applications. Wiley, New YorkMATH
33.
Samet S, Miri A (2012) Privacy-preserving back-propagation and extreme learning machine algorithms. Data Knowl Eng 79–80:40–61CrossRef
34.
Singh R, Balasundaram S (2007) Application of extreme learning machine for time series analysis. Int J Intell Technol 2:256–262
35.
Sjoberg J, Zhang Q, Ljung L, Berveniste A, Delyon B, Glorennec P, Hjalmarsson H, Juditsky (1995) Nonlinear black-box modeling in system identification: a unified overview. Automatica 31:1691–1724MathSciNetCrossRefMATH
36.
Souza LGM, Barreto GA (2006) Nonlinear system identification using local ARX models based on the self-organizing map. Learning and Nonlinear Models-Revista da Sociedade Brasileira de Redes Neurals (SBRN) 4(2):112–123CrossRef
37.
Vapnik VN (2000) The nature of statistical learning theory, 2nd edn. Springer, New YorkCrossRefMATH
38.
Wang X, Shao Q, Qing M, Zhai J (2013) Architecture selection for networks trained with extreme learning machine using localized generalization error model. Neurocomputing 102:3–9CrossRef
39.
Wang X, Chen A, Feng H (2011) Upper integral network with extreme learning mechanism. Neurocomputing 74(16):2520–2525CrossRef
40.
Wang XX, Chen S, Lowe D, Harris CJ (2006) Sparse support vector regression based on orthogonal forward selection for generalized kernel model. Neurocomputing 70:462–474CrossRef
41.
Yuan Y, Wang Y, Cao F (2011) Optimization approximation solution for regression problem based on extreme learning machine. Neurocomputing 74:2475–2482CrossRef
42.
Zhai J, Xu H, Wang X (2012) Dynamic ensemble extreme learning machine based on sample entropy. Soft Comput 16(9):1493–1502CrossRef
43.
Zhou GL, Toh KC (2005) Super linear convergence of a newton-type algorithm for monotone equations. J Optim Theory Appl 125:205–221MathSciNetCrossRefMATH
44.
Zhu Q-Y, Qin AK, Suganthan PN, Huang G-B (2005) Evolutionary extreme learning machine. Pattern Recogn 38:1759–1763CrossRefMATH
Metadaten
Titel
On optimization based extreme learning machine in primal for regression and classification by functional iterative method
verfasst von
S. Balasundaram
Deepak Gupta
Publikationsdatum
01.10.2016
Verlag
Springer Berlin Heidelberg
Erschienen in
International Journal of Machine Learning and Cybernetics / Ausgabe 5/2016
Print ISSN: 1868-8071
Elektronische ISSN: 1868-808X
DOI
https://doi.org/10.1007/s13042-014-0283-8

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