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International Journal of Machine Learning and Cybernetics
Erschienen in: International Journal of Machine Learning and Cybernetics 2/2011

01.06.2011 | Original Article

Extreme learning machines: a survey

verfasst von: Guang-Bin Huang, Dian Hui Wang, Yuan Lan

Erschienen in: International Journal of Machine Learning and Cybernetics | Ausgabe 2/2011

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Abstract

Computational intelligence techniques have been used in wide applications. Out of numerous computational intelligence techniques, neural networks and support vector machines (SVMs) have been playing the dominant roles. However, it is known that both neural networks and SVMs face some challenging issues such as: (1) slow learning speed, (2) trivial human intervene, and/or (3) poor computational scalability. Extreme learning machine (ELM) as emergent technology which overcomes some challenges faced by other techniques has recently attracted the attention from more and more researchers. ELM works for generalized single-hidden layer feedforward networks (SLFNs). The essence of ELM is that the hidden layer of SLFNs need not be tuned. Compared with those traditional computational intelligence techniques, ELM provides better generalization performance at a much faster learning speed and with least human intervene. This paper gives a survey on ELM and its variants, especially on (1) batch learning mode of ELM, (2) fully complex ELM, (3) online sequential ELM, (4) incremental ELM, and (5) ensemble of ELM.
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Literatur
1.
Rumelhart DE, Hinton GE, Williams RJ (1986) Learning representations by back-propagation errors. Nature 323:533–536CrossRef
2.
Cortes C, Vapnik V (1995) Support vector networks. Mach Learn 20(3):273–297MATH
3.
Rosenblatt F (1962) Principles of neurodynamics: perceptrons and the theory of brain mechanisms. Spartan Books, New YorkMATH
4.
Lowe D (1989) Adaptive radial basis function nonlinearities and the problem of generalisation. In: Proceedings of first IEE international conference on artificial neural networks, pp 171–175
5.
Huang G-B, Zhu Q-Y, Siew C-K (2004) Extreme learning machine: a new learning scheme of feedforward neural networks. In: Proceedings of international joint conference on neural networks (IJCNN2004), vol 2, Budapest, Hungary, 25–29 July 2004, pp 985–990
6.
Huang G-B, Zhu Q-Y, Siew C-K (2006) Extreme learning machine: theory and applications. Neurocomputing 70:489–501CrossRef
7.
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
8.
Huang G-B, Chen L (2007) Convex incremental extreme learning machine. Neurocomputing 70:3056–3062CrossRef
9.
Huang G-B, Chen L (2008) Enhanced random search based incremental extreme learning machine. Neurocomputing 71:3460–3468CrossRef
10.
Bartlett PL (1998) The sample complexity of pattern classification with neural networks: the size of the weights is more important than the size of the network. IEEE Trans Inf Theory 44(2):525–536MathSciNetMATHCrossRef
11.
Huang S-C, Huang Y-F (1991) Bounds on the number of hidden neurons in multilayer perceptrons. IEEE Trans Neural Netw 2(1):47–55CrossRef
12.
Sartori MA, Antsaklis PJ (1991) A simple method to derive bounds on the size and to train multilayer neural networks. IEEE Trans Neural Netw 2(4):467–471CrossRef
13.
Huang G-B, Babri HA (1998) Upper bounds on the number of hidden neurons in feedforward networks with arbitrary bounded nonlinear activation functions. IEEE Trans Neural Netw 9(1):224–229CrossRef
14.
Gallant A, White H (1992) There exists a neural network that does not make avoidable mistakes. In: White H (ed) Artificial neural networks: approximation and learning theory. Blackwell, Oxford, pp 5–11
15.
Hornik K (1991) Approximation capabilities of multilayer feedforward networks. Neural Netw 4:251–257CrossRef
16.
Leshno M, Lin VY, Pinkus A, Schocken S (1993) Multilayer feedforward networks with a nonpolynomial activation function can approximate any function. Neural Netw 6:861–867CrossRef
17.
Park J, Sandberg IW (1991) Universal approximation using radial-basis-function networks. Neural Comput 3:246–257CrossRef
18.
Hornik K, Stinchcombe M, White H (1989) Multilayer feedforward networks are universal approximators. Neural Netw 2:359–366CrossRef
19.
20.
Funahashi K (1989) On the approximate realization of continuous mappings by neural networks. Neural Netw 2:183–192CrossRef
21.
Stinchcombe M, White H (1992) Universal approximation using feedforward networks with non-sigmoid hidden layer activation functions. In: White H (ed) Artificial neural networks: approximation and learning theory. Blackwell, Oxford, pp 29–40
22.
Barron AR (1993) Universal approximation bounds for superpositions of a sigmoidal function. IEEE Trans Inf Theory 39(3):930–945MathSciNetMATHCrossRef
23.
Kwok T-Y, Yeung D-Y (1997) Objective functions for training new hidden units in constructive neural networks. IEEE Trans Neural Netw 8(5):1131–1148CrossRef
24.
Meir R, Maiorov VE (2000) On the optimality of neural-network approximation using incremental algorithms. IEEE Trans Neural Netw 11(2):323–337CrossRef
25.
26.
Huang G-B (2003) Learning capability and storage capacity of two-hidden-layer feedforward networks. IEEE Trans Neural Netw 14(2):274–281CrossRef
27.
Corwin EM, Logar AM, Oldham WJB (1994) An iterative method for training multilayer networks with threshold function. IEEE Trans Neural Netw 5(3):507–508CrossRef
28.
Toms DJ (1990) Training binary node feedforward neural networks by backpropagation of error. Electron Lett 26(21):1745–1746CrossRef
29.
Goodman RM, Zeng Z (1994) A learning algorithm for multi-layer perceptrons with hard-limiting threshold units. In: Proceedings of the 1994 IEEE workshop of neural networks for signal processing, pp 219–228
30.
Plagianakos VP, Magoulas GD, Nousis NK, Vrahatis MN (2001) Training multilayer networks with discrete activation functions. In: Proceedings of the IEEE international joint conference on neural networks (IJCNN’2001), Washington, DC, USA
31.
Voxman WL, Roy J, Goetschel H (1981) Advanced calculus: an introduction to modern analysis. Marcel Dekker, New York
32.
Broomhead DS, Lowe D (1988) Multivariable functional interpolation and adaptive networks. Complex Syst 2:321–355MathSciNetMATH
33.
Igelnik B, Pao YH (1995) Stochastic choice of basis functions in adaptive function approximation and the functional-link net. IEEE Trans Neural Netw 6(6):1320–1329CrossRef
34.
Huang G-B, Li M-B, Chen L, Siew C-K (2008) Incremental extreme learning machine with fully complex hidden nodes. Neurocomputing 71:576–583CrossRef
35.
Huang G-B, Siew C-K (2004) Extreme learning machine: RBF network case. In: Proceedings of the eighth international conference on control, automation, robotics and vision (ICARCV 2004), vol 2, Kunming, China, 6–9 Dec 2004, pp 1029–1036
36.
Huang G-B, Zhu Q-Y, Mao K-Z, Siew C-K, Saratchandran P, Sundararajan N (2006) Can threshold networks be trained directly?. IEEE Trans Circuits Syst II 53(3):187–191CrossRef
37.
Serre D (2002) Matrices: theory and applications. Springer, New YorkMATH
38.
Rao CR, Mitra SK (1971) Generalized inverse of matrices and its applications. Wiley, New YorkMATH
39.
Huang G-B, Zhou H, Ding X, Zhang R (2010) Extreme learning machine for regression and multi-class classification. IEEE Trans Pattern Anal Mach Intell (submitted)
40.
41.
42.
Deng W, Zheng Q, Chen L (2009) Regularized extreme learning machine. In: IEEE symposium on computational intelligence and data mining (CIDM2009), 30 March 2009–2 April 2009, pp 389–395
43.
Man Z, Lee K, Wang D, Cao Z, Miao C (2011) A new robust training algorithm for a class of single-hidden layer feedforward neural networks. Neurocomputing (in press)
44.
Miche Y, van Heeswijk M, Bas P, Simula O, Lendasse A (2011) TROP-ELM: a double-regularized elm using lars and tikhonov regularization. Neurocomputing (in press)
45.
Drucker H, Burges CJ, Kaufman L, Smola A, Vapnik V (1997) Support vector regression machines. In: Mozer M, Jordan J, Petscbe T (eds) Neural information processing systems, vol 9. MIT Press, Cambridge, pp 155–161
46.
Hsu C-W, Lin C-J (2002) A comparison of methods for multiclass support vector machines. IEEE Trans Neural Netw 13(2):415–425CrossRef
47.
Lin K-M, Lin C-J (2003) A study on reduced support vector machines. IEEE Trans Neural Netw 14(6):1449–1459CrossRef
48.
Lee Y-J, Mangasarian OL (2001) RSVM: reduced support vector machines. In: Proceedings of the SIAM international conference on data mining, Chicago, USA, 5–7 Apr 2001
49.
Suykens JAK, Vandewalle J (1997) Least squares support vector machine classifiers. Neural Process Lett 9(3):293–300CrossRef
50.
Frénay B, Verleysen M (2010) Using SVMs with randomised feature spaces: an extreme learning approach. In: Proceedings of the 18th European symposium on artificial neural networks (ESANN), Bruges, Belgium, 28–30 Apr 2010, pp 315–320
51.
Frénay B, Verleysen M (2011) Parameter-insensitive kernel in extreme learning for non-linear support vector regression. Neurocomputing (in press)
52.
Li M-B, Huang G-B, Saratchandran P, Sundararajan N (2005) Fully complex extreme learning machine. Neurocomputing 68:306–314CrossRef
53.
Cha I, Kassam SA (1995) Channel equalization using adaptive complex radial basis function networks. IEEE J Sel Areas Commun 13:122–131CrossRef
54.
Jianping D, Sundararajan N, Saratchandran P (2002) Communication channel equalization using complex-valued minimal radial basis function neural networks. IEEE Trans Neural Netw 13:687–696CrossRef
55.
Kim T, Adali T (2003) Approximation by fully complex multilayer perseptrons. Neural Comput 15:1641–1666MATHCrossRef
56.
LeCun Y, Bottou L, Orr GB, Müller K-R (1998) Efficient BackProp. Lect Notes Comput Sci 1524:9–50
57.
58.
Kadirkamanathan V, Niranjan M (1993) A function estimation approach to sequential learning with neural networks. Neural Comput 5:954–975CrossRef
59.
Yingwei L, Sundararajan N, Saratchandran P (1997) A sequential learning scheme for function approximation using minimal radial basis function (RBF) neural networks. Neural Comput 9:461–478MATHCrossRef
60.
Yingwei L, Sundararajan N, Saratchandran P (1998) Performance evaluation of a sequential minimal radial basis function (RBF) neural network learning algorithm. IEEE Trans Neural Netw 9(2):308–318CrossRef
61.
Salmerón M, Ortega J, Puntonet CG, Prieto A (2001) Improved RAN sequential prediction using orthogonal techniques. Neurocomputing 41:153–172
62.
Rojas I, Pomares H, Bernier JL, Ortega J, Pino B, Pelayo FJ, Prieto A (2002) Time series analysis using normalized PG-RBF network with regression weights. Neurocomputing 42:267–285MATHCrossRef
63.
Huang G-B, Saratchandran P, Sundararajan N (2004) An efficient sequential learning algorithm for growing and pruning RBF (GAP-RBF) networks. IEEE Trans Syst Man Cybern Part B 34(6):2284–2292CrossRef
64.
Huang G-B, Saratchandran P, Sundararajan N (2005) A generalized growing and pruning RBF (GGAP-RBF) neural network for function approximation. IEEE Trans Neural Netw 16(1):57–67CrossRef
65.
Liang N-Y, Huang G-B, Saratchandran P, Sundararajan N (2006) A fast and accurate on-line sequential learning algorithm for feedforward networks. IEEE Trans Neural Netw 17(6):1411–1423CrossRef
66.
Chong EKP,  Zak SH (2001) An introduction to optimization. Wiley, New YorkMATH
67.
Golub GH, Loan CFV (1996) Matrix computations, 3rd edn. The Johns Hopkins University Press, Baltimore
68.
Mackey MC, Glass L (1997) Oscillation and chaos in physiological control systems. Science 197:287–289CrossRef
69.
Vapnik VN (1998) Statistical learning theory. Wiley, New YorkMATH
70.
Smola A, Schölkopf B (1998) A tutorial on support vector regression. NeuroCOLT2 technical report NC2-TR-1998-030
71.
Hansen LK, Salamon P (1990) Neural network ensemble. IEEE Trans Pattern Anal Mach Intell 12(10):993–1001CrossRef
72.
73.
Schapire RE (1990) The strength of weak learnability. Mach Learn 5(2):197–227
74.
75.
Freund Y, Schapire RE (1997) A decision-theoretic generalization of online learning and an application to boosting. J Comput Syst Sci 55:119–139MathSciNetMATHCrossRef
76.
Sun Z-L, Choi T-M, Au K-F, Yu Y (2008) Sales forecasting using extreme learning machine with applications in fashion retailing. Decis Support Syst 46(1):411–419CrossRef
77.
van Heeswijk M, Miche Y, Lindh-Knuutila T, Hilbers PA, Honkela T, Oja E, Lendasse A (2009) Adaptive ensemble models of extreme learning machines for time series prediction. Lect Notes Comput Sci 5769:305–314CrossRef
78.
van Heeswijk M, Miche Y, Oja E, Lendasse A (2011) Gpu-accelerated and parallelized ELM ensembles for large-scale regression. Neurocomputing (in press)
79.
Minku FL, Inoue H, Yao X (2011) Negative correlation in incremental learning. Nat Comp (in press)
80.
Sun Y, Yuan Y, Wang G (2011) An OS-ELM based distributed ensemble classification framework in p2p networks. Neurocomputing (in press)
81.
Lan Y, Soh YC, Huang G-B (2009) Ensemble of online sequential extreme learning machine. Neurocomputing 72:3391–3395CrossRef
82.
Rong H-J, Ong Y-S, Tan A-H, Zhu Z (2008) A fast pruned-extreme learning machine for classification problem. Neurocomputing 72:359–366CrossRef
83.
Miche Y, Sorjamaa A, Lendasse A (2008) OP-ELM: theory, experiments and a toolbox. Lect Notes Comput Sci 5163:145–154CrossRef
84.
Simila T, Tikka J (2005) Multiresponse sparse regression with application to multidimensional scaling. In: Proceedings in artificial neural networks: formal models and their applications, ICANN 2005, vol 3697, pp 97–102
85.
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
86.
Lan Y, Soh YC, Huang G-B (2010) Random search enhancement of error minimized extreme learning machine. In: European symposium on artificial neural networks (ESANN 2010), Bruges, Belgium, Apr 2010, pp 327–332
87.
Li K, Huang G-B, Ge SS (2010) Fast construction of single hidden layer feedforward networks. In: Rozenberg G, Bäck T, Kok JN (eds) Handbook of natural computing. Springer, Berlin, Mar 2010
88.
Mao K-Z, Bilings SA (1997) Algorithms for minimal model structure detection in nonlinear dynamic system identification. Int J Control 68(2):311–330MATHCrossRef
89.
Lan Y, Soh YC, Huang G-B (2010) Constructive hidden nodes selection of extreme learning machine for regression. Neurocomputing 73:3191–3199CrossRef
90.
Lan Y, Soh YC, Huang GB (2010) Two-stage extreme learning machine for regression. Neurocomputing 73:3028–3038CrossRef
91.
Liu Q, He Q, Shi Z (2008) Extreme support vector machine classifier. Lect Notes Comput Sci 5012:222–233CrossRef
92.
Huang G-B, Ding X, Zhou H (2010) Optimization method based extreme learning machine for classification. Neurocomputing 74:155–163CrossRef
93.
Fletcher R (1981) Practical methods of optimization. In: Constrained optimization, vol 2. Wiley, New York
94.
Handoko SD, Keong KC, Soon OY, Zhang GL, Brusic V (2006) Extreme learning machine for predicting hla-peptide binding. Lect Notes Comput Sci 3973:716–721CrossRef
95.
Sun Z-L, Au K-F, Choi T-M (2008) A neuro-fuzzy inference system through integration of fuzzy logic and extreme learning machines. IEEE Trans Syst Man Cybern Part B Cybern 37(5):1321–1331CrossRef
96.
Tang X, Han M (2009) Partial lanczos extreme learning machine for single-output regression problems. Neurocomputing 72(13-15):3066–3076CrossRef
97.
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
98.
Yeu C-WT, Lim M-H, Huang G-B, Agarwal A, Ong Y-S (2006) A new machine learning paradigm for terrain reconstruction. IEEE Geosci Remote Sens Lett 3(3):382–386CrossRef
99.
Soria-Olivas E, Gomez-Sanchis J, Martin JD, Vila-Frances J, Martinez M, Magdalena JR, Serrano AJ (2011) BELM: Bayesian extreme learning machine. IEEE Trans Neural Netw 22(3):505–509CrossRef
100.
Xu Y, Dong ZY, Meng K, Zhang R, Wong KP (2011) Real-time transient stability assessment model using extreme learning machine. IET Gener Transm Distrib 5(3):314–322CrossRef
101.
Barea R, Boquete L, Rodriguez-Ascariz JM, Ortega S, Lopez E (2011) Sensory system for implementing a human-computer interface based on electrooculography. Sensors 11(1):310–328CrossRef
102.
Chang N-B, Han M, Yao W, Chen L-C, Xu S (2011) Change detection of land use and land cover in an urban region with SPOT-5 images and partial lanczos extreme learning machine. J Appl Remote Sens 4
103.
Saraswathi S, Sundaram S, Sundararajan N, Zimmermann M, Nilsen-Hamilton M (2011) ICGA-PSO-ELM approach for accurate multiclass cancer classification resulting in reduced gene sets in which genes encoding secreted proteins are highly represented. IEEE ACM Trans Comput Biol Bioinforma 6(2):452–463CrossRef
104.
Li F-C, Wang P-K, Wang G-E (2009) Comparison of the primitive classifiers with extreme learning machine in credit scoring. In: 2009 IEEE international conference on industrial engineering and engineering management, pp 685–688
105.
Choi K, Toh K-A, Byun H (2011) Realtime training on mobile devices for face recognition applications. Pattern Recogn 44(2):386–400
106.
Chen FL, Ou TY (2011) Sales forecasting system based on gray extreme learning machine with Taguchi method in retail industry. Expert Syst Appl 38(3):1336–1345CrossRef
107.
Ye Y, Squartim S, Piazza F (2010) Incremental-based extreme learning machine algorithms for time-variant neural networks. Lect Notes Comput Sci 6215:9–16CrossRef
108.
Suresh S, Saraswathi S, Sundararajan N (2010) Performance enhancement of extreme learning machine for multi-category sparse data classification problems. Eng Appl Artif Intell 23(7):1149–1157CrossRef
109.
Li G, Liu M, Dong M (2010) A new online learning algorithm for structure-adjustable extreme learning machine. Comput Math Appl 60(3):377–389MathSciNetMATHCrossRef
110.
Liu Y, Xu X, Wang C (2009) Simple ensemble of extreme learning machine. In: Proceedings of the 2009 2nd international congress on image and signal processing, pp 2177–2181
111.
Deng W, Chen L (2010) Color image watermarking using regularized extreme learning machine. Neural Network World 20(3):317–330
112.
Mohammed AA, Wu QMJ, Sid-Ahmed MA (2010) Application of wave atoms decomposition and extreme learning machine for fingerprint classification. Lect Notes Comput Sci 6112:246–256CrossRef
113.
Minhas R, Baradarani A, Seifzadeh S, Wu QMJ (2010) Human action recognition using extreme learning machine based on visual vocabularies. Neurocomputing 73:1906–1917CrossRef
114.
Malathi V, Marimuthu NS, Baskar S (2010) Intelligent approaches using support vector machine and extreme learning machine for transmission line protection. Neurocomputing 73:2160–2167CrossRef
115.
Tang X-L, Han M (2010) Ternary reversible extreme learning machines: the incremental tri-training method for semi-supervised classification. Knowl Inf Syst 22(3):345–372CrossRef
116.
Nizar AH, Dong ZY, Wang Y (2008) Power utility nontechnical loss analysis with extreme learning machine method. IEEE Trans Power Syst 23(3):946–955CrossRef
117.
Cho JS, White H (2011) Testing correct model specification using extreme learning machines. Neurocomputing (in press)
118.
Wang Y, Cao F, Yuan Y (2011) A study on effectiveness of extreme learning machine. Neurocomputing (in press)
119.
Deng J, Li K, Irwin GW (2011) Fast automatic two-stage nonlinear model identification based on the extreme learning machine. Neurocomputing (in press)
Metadaten
Titel
Extreme learning machines: a survey
verfasst von
Guang-Bin Huang
Dian Hui Wang
Yuan Lan
Publikationsdatum
01.06.2011
Verlag
Springer-Verlag
Erschienen in
International Journal of Machine Learning and Cybernetics / Ausgabe 2/2011
Print ISSN: 1868-8071
Elektronische ISSN: 1868-808X
DOI
https://doi.org/10.1007/s13042-011-0019-y

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