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Volumn 2, Issue 2, 2011, Pages 107-122

Extreme learning machines: A survey

Author keywords

ELM feature space; ELM kernel; Ensemble; Extreme learning machine; Incremental learning; Online sequential learning; Support vector machine

Indexed keywords

ELM KERNEL; ENSEMBLE; EXTREME LEARNING MACHINE; FEATURE SPACE; INCREMENTAL LEARNING; SEQUENTIAL LEARNING; SUPPORT VECTOR;

EID: 79958178274     PISSN: 18688071     EISSN: 1868808X     Source Type: Journal    
DOI: 10.1007/s13042-011-0019-y     Document Type: Article
Times cited : (1841)

References (119)
  • 1
    • 0022471098 scopus 로고
    • Learning representations by back-propagation errors
    • Rumelhart DE, Hinton GE, Williams RJ (1986) Learning representations by back-propagation errors. Nature 323: 533-536.
    • (1986) Nature , vol.323 , pp. 533-536
    • Rumelhart, D.E.1    Hinton, G.E.2    Williams, R.J.3
  • 2
    • 34249753618 scopus 로고
    • Support vector networks
    • Cortes C, Vapnik V (1995) Support vector networks. Mach Learn 20(3): 273-297.
    • (1995) Mach Learn , vol.20 , Issue.3 , pp. 273-297
    • Cortes, C.1    Vapnik, V.2
  • 6
    • 33745903481 scopus 로고    scopus 로고
    • Extreme learning machine: theory and applications
    • Huang G-B, Zhu Q-Y, Siew C-K (2006) Extreme learning machine: theory and applications. Neurocomputing 70: 489-501.
    • (2006) Neurocomputing , vol.70 , pp. 489-501
    • Huang, G.-B.1    Zhu, Q.-Y.2    Siew, C.-K.3
  • 7
    • 33745918399 scopus 로고    scopus 로고
    • Universal approximation using incremental constructive feedforward networks with random hidden nodes
    • 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): 879-892.
    • (2006) IEEE Trans Neural Netw , vol.17 , Issue.4 , pp. 879-892
    • Huang, G.-B.1    Chen, L.2    Siew, C.-K.3
  • 8
    • 34548158996 scopus 로고    scopus 로고
    • Convex incremental extreme learning machine
    • Huang G-B, Chen L (2007) Convex incremental extreme learning machine. Neurocomputing 70: 3056-3062.
    • (2007) Neurocomputing , vol.70 , pp. 3056-3062
    • Huang, G.B.1    Chen, L.2
  • 9
    • 56549090053 scopus 로고    scopus 로고
    • Enhanced random search based incremental extreme learning machine
    • Huang G-B, Chen L (2008) Enhanced random search based incremental extreme learning machine. Neurocomputing 71: 3460-3468.
    • (2008) Neurocomputing , vol.71 , pp. 3460-3468
    • Huang, G.B.1    Chen, L.2
  • 10
    • 0032028728 scopus 로고    scopus 로고
    • The sample complexity of pattern classification with neural networks: the size of the weights is more important than the size of the network
    • 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): 525-536.
    • (1998) IEEE Trans Inf Theory , vol.44 , Issue.2 , pp. 525-536
    • Bartlett, P.L.1
  • 11
    • 0025792215 scopus 로고
    • Bounds on the number of hidden neurons in multilayer perceptrons
    • Huang S-C, Huang Y-F (1991) Bounds on the number of hidden neurons in multilayer perceptrons. IEEE Trans Neural Netw 2(1): 47-55.
    • (1991) IEEE Trans Neural Netw , vol.2 , Issue.1 , pp. 47-55
    • Huang, S.C.1    Huang, Y.F.2
  • 12
    • 0026190194 scopus 로고
    • A simple method to derive bounds on the size and to train multilayer neural networks
    • 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): 467-471.
    • (1991) IEEE Trans Neural Netw , vol.2 , Issue.4 , pp. 467-471
    • Sartori, M.A.1    Antsaklis, P.J.2
  • 13
    • 0031673055 scopus 로고    scopus 로고
    • Upper bounds on the number of hidden neurons in feedforward networks with arbitrary bounded nonlinear activation functions
    • 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): 224-229.
    • (1998) IEEE Trans Neural Netw , vol.9 , Issue.1 , pp. 224-229
    • Huang, G.B.1    Babri, H.A.2
  • 15
    • 0025751820 scopus 로고
    • Approximation capabilities of multilayer feedforward networks
    • Hornik K (1991) Approximation capabilities of multilayer feedforward networks. Neural Netw 4: 251-257.
    • (1991) Neural Netw , vol.4 , pp. 251-257
    • Hornik, K.1
  • 16
    • 0027262895 scopus 로고
    • Multilayer feedforward networks with a nonpolynomial activation function can approximate any function
    • Leshno M, Lin VY, Pinkus A, Schocken S (1993) Multilayer feedforward networks with a nonpolynomial activation function can approximate any function. Neural Netw 6: 861-867.
    • (1993) Neural Netw , vol.6 , pp. 861-867
    • Leshno, M.1    Lin, V.Y.2    Pinkus, A.3    Schocken, S.4
  • 17
    • 0000106040 scopus 로고
    • Universal approximation using radial-basis-function networks
    • Park J, Sandberg IW (1991) Universal approximation using radial-basis-function networks. Neural Comput 3: 246-257.
    • (1991) Neural Comput , vol.3 , pp. 246-257
    • Park, J.1    Sandberg, I.W.2
  • 18
    • 0024880831 scopus 로고
    • Multilayer feedforward networks are universal approximators
    • Hornik K, Stinchcombe M, White H (1989) Multilayer feedforward networks are universal approximators. Neural Netw 2: 359-366.
    • (1989) Neural Netw , vol.2 , pp. 359-366
    • Hornik, K.1    Stinchcombe, M.2    White, H.3
  • 19
    • 0024861871 scopus 로고
    • Approximation by superpositions of a sigmoidal function
    • Cybenko G (1989) Approximation by superpositions of a sigmoidal function. Math Control Signals Syst 2(4): 303-314.
    • (1989) Math Control Signals Syst , vol.2 , Issue.4 , pp. 303-314
    • Cybenko, G.1
  • 20
    • 0024866495 scopus 로고
    • On the approximate realization of continuous mappings by neural networks
    • Funahashi K (1989) On the approximate realization of continuous mappings by neural networks. Neural Netw 2: 183-192.
    • (1989) Neural Netw , vol.2 , pp. 183-192
    • Funahashi, K.1
  • 22
    • 0027599793 scopus 로고
    • Universal approximation bounds for superpositions of a sigmoidal function
    • Barron AR (1993) Universal approximation bounds for superpositions of a sigmoidal function. IEEE Trans Inf Theory 39(3): 930-945.
    • (1993) IEEE Trans Inf Theory , vol.39 , Issue.3 , pp. 930-945
    • Barron, A.R.1
  • 23
    • 0031236099 scopus 로고    scopus 로고
    • Objective functions for training new hidden units in constructive neural networks
    • Kwok T-Y, Yeung D-Y (1997) Objective functions for training new hidden units in constructive neural networks. IEEE Trans Neural Netw 8(5): 1131-1148.
    • (1997) IEEE Trans Neural Netw , vol.8 , Issue.5 , pp. 1131-1148
    • Kwok, T.-Y.1    Yeung, D.-Y.2
  • 24
    • 0033732457 scopus 로고    scopus 로고
    • On the optimality of neural-network approximation using incremental algorithms
    • Meir R, Maiorov VE (2000) On the optimality of neural-network approximation using incremental algorithms. IEEE Trans Neural Netw 11(2): 323-337.
    • (2000) IEEE Trans Neural Netw , vol.11 , Issue.2 , pp. 323-337
    • Meir, R.1    Maiorov, V.E.2
  • 25
    • 33746898284 scopus 로고    scopus 로고
    • Function approximation with SAOCIF: a general sequential method and a particular algorithm with feed-forward neural networks
    • Universitat Politècnica de Catalunya
    • Romero E (2001) Function approximation with SAOCIF: a general sequential method and a particular algorithm with feed-forward neural networks. Departament de Llenguatges i Sistemes Informàtics, Universitat Politècnica de Catalunya. http://www. lsi. upc. es/dept/techreps/html/R01-41. html.
    • (2001) Departament de Llenguatges i Sistemes Informàtics
    • Romero, E.1
  • 26
    • 0037361264 scopus 로고    scopus 로고
    • Learning capability and storage capacity of two-hidden-layer feedforward networks
    • Huang G-B (2003) Learning capability and storage capacity of two-hidden-layer feedforward networks. IEEE Trans Neural Netw 14(2): 274-281.
    • (2003) IEEE Trans Neural Netw , vol.14 , Issue.2 , pp. 274-281
    • Huang, G.-B.1
  • 27
    • 0028425156 scopus 로고
    • An iterative method for training multilayer networks with threshold function
    • Corwin EM, Logar AM, Oldham WJB (1994) An iterative method for training multilayer networks with threshold function. IEEE Trans Neural Netw 5(3): 507-508.
    • (1994) IEEE Trans Neural Netw , vol.5 , Issue.3 , pp. 507-508
    • Corwin, E.M.1    Logar, A.M.2    Oldham, W.J.B.3
  • 28
    • 0025505772 scopus 로고
    • Training binary node feedforward neural networks by backpropagation of error
    • Toms DJ (1990) Training binary node feedforward neural networks by backpropagation of error. Electron Lett 26(21): 1745-1746.
    • (1990) Electron Lett , vol.26 , Issue.21 , pp. 1745-1746
    • Toms, D.J.1
  • 32
    • 0000621802 scopus 로고
    • Multivariable functional interpolation and adaptive networks
    • Broomhead DS, Lowe D (1988) Multivariable functional interpolation and adaptive networks. Complex Syst 2: 321-355.
    • (1988) Complex Syst , vol.2 , pp. 321-355
    • Broomhead, D.S.1    Lowe, D.2
  • 33
    • 0029403793 scopus 로고
    • Stochastic choice of basis functions in adaptive function approximation and the functional-link net
    • 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): 1320-1329.
    • (1995) IEEE Trans Neural Netw , vol.6 , Issue.6 , pp. 1320-1329
    • Igelnik, B.1    Pao, Y.H.2
  • 34
    • 38649131505 scopus 로고    scopus 로고
    • Incremental extreme learning machine with fully complex hidden nodes
    • Huang G-B, Li M-B, Chen L, Siew C-K (2008) Incremental extreme learning machine with fully complex hidden nodes. Neurocomputing 71: 576-583.
    • (2008) Neurocomputing , vol.71 , pp. 576-583
    • Huang, G.-B.1    Li, M.-B.2    Chen, L.3    Siew, C.K.4
  • 35
    • 21244456913 scopus 로고    scopus 로고
    • Extreme learning machine: RBF network case
    • automation, robotics and vision (ICARCV 2004), Kunming, China, 6-9 Dec 2004
    • 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.
    • (2004) In: Proceedings of the eighth international conference on control , vol.2 , pp. 1029-1036
    • Huang, G.-B.1    Siew, C.-K.2
  • 40
    • 84942484786 scopus 로고
    • Ridge regression: biased estimation for nonorthogonal problems
    • Hoerl AE, Kennard RW (1970) Ridge regression: biased estimation for nonorthogonal problems. Technometrics 12(1): 55-67.
    • (1970) Technometrics , vol.12 , Issue.1 , pp. 55-67
    • Hoerl, A.E.1    Kennard, R.W.2
  • 41
    • 45749126424 scopus 로고    scopus 로고
    • Deterministic neural classification
    • Toh K-A (2008) Deterministic neural classification. Neural Comput 20(6): 1565-1595.
    • (2008) Neural Comput , vol.20 , Issue.6 , pp. 1565-1595
    • Toh, K.A.1
  • 45
    • 84899013173 scopus 로고    scopus 로고
    • In: Mozer M, Jordan J, Petscbe T (eds) Neural information processing systems, MIT Press, Cambridge
    • 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.
    • (1997) Support vector regression machines , vol.9 , pp. 155-161
    • Drucker, H.1    Burges, C.J.2    Kaufman, L.3    Smola, A.4    Vapnik, V.5
  • 46
    • 0036505670 scopus 로고    scopus 로고
    • A comparison of methods for multiclass support vector machines
    • Hsu C-W, Lin C-J (2002) A comparison of methods for multiclass support vector machines. IEEE Trans Neural Netw 13(2): 415-425.
    • (2002) IEEE Trans Neural Netw , vol.13 , Issue.2 , pp. 415-425
    • Hsu, C.-W.1    Lin, C.-J.2
  • 47
    • 0742321291 scopus 로고    scopus 로고
    • A study on reduced support vector machines
    • Lin K-M, Lin C-J (2003) A study on reduced support vector machines. IEEE Trans Neural Netw 14(6): 1449-1459.
    • (2003) IEEE Trans Neural Netw , vol.14 , Issue.6 , pp. 1449-1459
    • Lin, K.M.1    Lin, C.J.2
  • 49
    • 0032638628 scopus 로고    scopus 로고
    • Least squares support vector machine classifiers
    • Suykens JAK, Vandewalle J (1997) Least squares support vector machine classifiers. Neural Process Lett 9(3): 293-300.
    • (1997) Neural Process Lett , vol.9 , Issue.3 , pp. 293-300
    • Suykens, J.A.K.1    Vandewalle, J.2
  • 53
    • 0029205963 scopus 로고
    • Channel equalization using adaptive complex radial basis function networks
    • Cha I, Kassam SA (1995) Channel equalization using adaptive complex radial basis function networks. IEEE J Sel Areas Commun 13: 122-131.
    • (1995) IEEE J Sel Areas Commun , vol.13 , pp. 122-131
    • Cha, I.1    Kassam, S.A.2
  • 54
    • 0036565527 scopus 로고    scopus 로고
    • Communication channel equalization using complex-valued minimal radial basis function neural networks
    • Jianping D, Sundararajan N, Saratchandran P (2002) Communication channel equalization using complex-valued minimal radial basis function neural networks. IEEE Trans Neural Netw 13: 687-696.
    • (2002) IEEE Trans Neural Netw , vol.13 , pp. 687-696
    • Jianping, D.1    Sundararajan, N.2    Saratchandran, P.3
  • 55
    • 0038159963 scopus 로고    scopus 로고
    • Approximation by fully complex multilayer perseptrons
    • Kim T, Adali T (2003) Approximation by fully complex multilayer perseptrons. Neural Comput 15: 1641-1666.
    • (2003) Neural Comput , vol.15 , pp. 1641-1666
    • Kim, T.1    Adali, T.2
  • 57
    • 0001071040 scopus 로고
    • A resource-allocating network for function interpolation
    • Platt J (1991) A resource-allocating network for function interpolation. Neural Comput 3: 213-225.
    • (1991) Neural Comput , vol.3 , pp. 213-225
    • Platt, J.1
  • 58
    • 0001553560 scopus 로고
    • A function estimation approach to sequential learning with neural networks
    • Kadirkamanathan V, Niranjan M (1993) A function estimation approach to sequential learning with neural networks. Neural Comput 5: 954-975.
    • (1993) Neural Comput , vol.5 , pp. 954-975
    • Kadirkamanathan, V.1    Niranjan, M.2
  • 59
    • 0031568361 scopus 로고    scopus 로고
    • A sequential learning scheme for function approximation using minimal radial basis function (RBF) neural networks
    • 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: 461-478.
    • (1997) Neural Comput , vol.9 , pp. 461-478
    • Yingwei, L.1    Sundararajan, N.2    Saratchandran, P.3
  • 60
    • 0032022388 scopus 로고    scopus 로고
    • Performance evaluation of a sequential minimal radial basis function (RBF) neural network learning algorithm
    • 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): 308-318.
    • (1998) IEEE Trans Neural Netw , vol.9 , Issue.2 , pp. 308-318
    • Yingwei, L.1    Sundararajan, N.2    Saratchandran, P.3
  • 63
    • 10044221078 scopus 로고    scopus 로고
    • An efficient sequential learning algorithm for growing and pruning RBF (GAP-RBF) networks
    • 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): 2284-2292.
    • (2004) IEEE Trans Syst Man Cybern Part B , vol.34 , Issue.6 , pp. 2284-2292
    • Huang, G.-B.1    Saratchandran, P.2    Sundararajan, N.3
  • 64
    • 13844256702 scopus 로고    scopus 로고
    • A generalized growing and pruning RBF (GGAP-RBF) neural network for function approximation
    • 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): 57-67.
    • (2005) IEEE Trans Neural Netw , vol.16 , Issue.1 , pp. 57-67
    • Huang, G.-B.1    Saratchandran, P.2    Sundararajan, N.3
  • 65
    • 34047174077 scopus 로고    scopus 로고
    • A fast and accurate on-line sequential learning algorithm for feedforward networks
    • 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): 1411-1423.
    • (2006) IEEE Trans Neural Netw , vol.17 , Issue.6 , pp. 1411-1423
    • Liang, N.-Y.1    Huang, G.-B.2    Saratchandran, P.3    Sundararajan, N.4
  • 68
    • 0017714604 scopus 로고    scopus 로고
    • Oscillation and chaos in physiological control systems
    • Mackey MC, Glass L (1997) Oscillation and chaos in physiological control systems. Science 197: 287-289.
    • (1997) Science , vol.197 , pp. 287-289
    • Mackey, M.C.1    Glass, L.2
  • 72
    • 0030211964 scopus 로고    scopus 로고
    • Bagging predictor
    • Breiman L (1996) Bagging predictor. Mach Learn 24(2): 123-140.
    • (1996) Mach Learn , vol.24 , Issue.2 , pp. 123-140
    • Breiman, L.1
  • 73
    • 0025448521 scopus 로고
    • The strength of weak learnability
    • Schapire RE (1990) The strength of weak learnability. Mach Learn 5(2): 197-227.
    • (1990) Mach Learn , vol.5 , Issue.2 , pp. 197-227
    • Schapire, R.E.1
  • 74
    • 58149321460 scopus 로고
    • Boosting a weak algorithm by majority
    • Freund Y (1995) Boosting a weak algorithm by majority. Inf Comput 121(2): 256-285.
    • (1995) Inf Comput , vol.121 , Issue.2 , pp. 256-285
    • Freund, Y.1
  • 75
    • 0031211090 scopus 로고    scopus 로고
    • A decision-theoretic generalization of online learning and an application to boosting
    • Freund Y, Schapire RE (1997) A decision-theoretic generalization of online learning and an application to boosting. J Comput Syst Sci 55: 119-139.
    • (1997) J Comput Syst Sci , vol.55 , pp. 119-139
    • Freund, Y.1    Schapire, R.E.2
  • 76
    • 56049098499 scopus 로고    scopus 로고
    • Sales forecasting using extreme learning machine with applications in fashion retailing
    • 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): 411-419.
    • (2008) Decis Support Syst , vol.46 , Issue.1 , pp. 411-419
    • Sun, Z.L.1    Choi, T.M.2    Au, K.F.3    Yu, Y.4
  • 81
    • 77954299719 scopus 로고    scopus 로고
    • Ensemble of online sequential extreme learning machine
    • Lan Y, Soh YC, Huang G-B (2009) Ensemble of online sequential extreme learning machine. Neurocomputing 72: 3391-3395.
    • (2009) Neurocomputing , vol.72 , pp. 3391-3395
    • Lan, Y.1    Soh, Y.C.2    Huang, G.B.3
  • 82
    • 55949132682 scopus 로고    scopus 로고
    • A fast pruned-extreme learning machine for classification problem
    • Rong H-J, Ong Y-S, Tan A-H, Zhu Z (2008) A fast pruned-extreme learning machine for classification problem. Neurocomputing 72: 359-366.
    • (2008) Neurocomputing , vol.72 , pp. 359-366
    • Rong, H.J.1    Ong, Y.S.2    Tan, A.H.3    Zhu, Z.4
  • 85
    • 68949200808 scopus 로고    scopus 로고
    • Error minimized extreme learning machine with growth of hidden nodes and incremental learning
    • 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): 1352-1357.
    • (2009) IEEE Trans Neural Netw , vol.20 , Issue.8 , pp. 1352-1357
    • Feng, G.1    Huang, G.B.2    Lin, Q.3    Gay, R.4
  • 86
    • 79958181023 scopus 로고    scopus 로고
    • Random search enhancement of error minimized extreme learning machine
    • ESANN 2010), Bruges, Belgium, Apr 2010
    • 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.
    • (2010) In: European symposium on artificial neural networks , pp. 327-332
    • Lan, Y.1    Soh, Y.C.2    Huang, G.-B.3
  • 88
    • 0031224757 scopus 로고    scopus 로고
    • Algorithms for minimal model structure detection in nonlinear dynamic system identification
    • Mao K-Z, Bilings SA (1997) Algorithms for minimal model structure detection in nonlinear dynamic system identification. Int J Control 68(2): 311-330.
    • (1997) Int J Control , vol.68 , Issue.2 , pp. 311-330
    • Mao, K.Z.1    Bilings, S.A.2
  • 89
    • 78650310346 scopus 로고    scopus 로고
    • Constructive hidden nodes selection of extreme learning machine for regression
    • Lan Y, Soh YC, Huang G-B (2010) Constructive hidden nodes selection of extreme learning machine for regression. Neurocomputing 73: 3191-3199.
    • (2010) Neurocomputing , vol.73 , pp. 3191-3199
    • Lan, Y.1    Soh, Y.C.2    Huang, G.-B.3
  • 90
    • 78650010629 scopus 로고    scopus 로고
    • Two-stage extreme learning machine for regression
    • Lan Y, Soh YC, Huang GB (2010) Two-stage extreme learning machine for regression. Neurocomputing 73: 3028-3038.
    • (2010) Neurocomputing , vol.73 , pp. 3028-3038
    • Lan, Y.1    Soh, Y.C.2    Huang, G.B.3
  • 91
    • 44649099490 scopus 로고    scopus 로고
    • Extreme support vector machine classifier
    • Liu Q, He Q, Shi Z (2008) Extreme support vector machine classifier. Lect Notes Comput Sci 5012: 222-233.
    • (2008) Lect Notes Comput Sci , vol.5012 , pp. 222-233
    • Liu, Q.1    He, Q.2    Shi, Z.3
  • 92
    • 78649492473 scopus 로고    scopus 로고
    • Optimization method based extreme learning machine for classification
    • Huang G-B, Ding X, Zhou H (2010) Optimization method based extreme learning machine for classification. Neurocomputing 74: 155-163.
    • (2010) Neurocomputing , vol.74 , pp. 155-163
    • Huang, G.-B.1    Ding, X.2    Zhou, H.3
  • 93
    • 0003355189 scopus 로고
    • Practical methods of optimization
    • Wiley, New York
    • Fletcher R (1981) Practical methods of optimization. In: Constrained optimization, vol 2. Wiley, New York.
    • (1981) In: Constrained optimization , vol.2
    • Fletcher, R.1
  • 95
    • 35148882421 scopus 로고    scopus 로고
    • A neuro-fuzzy inference system through integration of fuzzy logic and extreme learning machines
    • 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): 1321-1331.
    • (2008) IEEE Trans Syst Man Cybern Part B Cybern , vol.37 , Issue.5 , pp. 1321-1331
    • Sun, Z.-L.1    Au, K.-F.2    Choi, T.-M.3
  • 96
    • 78049530568 scopus 로고    scopus 로고
    • Partial lanczos extreme learning machine for single-output regression problems
    • Tang X, Han M (2009) Partial lanczos extreme learning machine for single-output regression problems. Neurocomputing 72(13-15): 3066-3076.
    • (2009) Neurocomputing , vol.72 , Issue.13-15 , pp. 3066-3076
    • Tang, X.1    Han, M.2
  • 100
    • 79951521183 scopus 로고    scopus 로고
    • Real-time transient stability assessment model using extreme learning machine
    • 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): 314-322.
    • (2011) IET Gener Transm Distrib , vol.5 , Issue.3 , pp. 314-322
    • Xu, Y.1    Dong, Z.Y.2    Meng, K.3    Zhang, R.4    Wong, K.P.5
  • 101
    • 79251647541 scopus 로고    scopus 로고
    • Sensory system for implementing a human-computer interface based on electrooculography
    • 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): 310-328.
    • (2011) Sensors , vol.11 , Issue.1 , pp. 310-328
    • Barea, R.1    Boquete, L.2    Rodriguez-Ascariz, J.M.3    Ortega, S.4    Lopez, E.5
  • 102
    • 80155147984 scopus 로고    scopus 로고
    • Change detection of land use and land cover in an urban region with SPOT-5 images and partial lanczos extreme learning machine
    • 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.
    • (2011) J Appl Remote Sens 4
    • Chang, N.-B.1    Han, M.2    Yao, W.3    Chen, L.-C.4    Xu, S.5
  • 103
    • 79551661137 scopus 로고    scopus 로고
    • ICGA-PSO-ELM approach for accurate multiclass cancer classification resulting in reduced gene sets in which genes encoding secreted proteins are highly represented
    • 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): 452-463.
    • (2011) IEEE ACM Trans Comput Biol Bioinforma , vol.6 , Issue.2 , pp. 452-463
    • Saraswathi, S.1    Sundaram, S.2    Sundararajan, N.3    Zimmermann, M.4    Nilsen-Hamilton, M.5
  • 106
    • 78049526620 scopus 로고    scopus 로고
    • Sales forecasting system based on gray extreme learning machine with Taguchi method in retail industry
    • 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): 1336-1345.
    • (2011) Expert Syst Appl , vol.38 , Issue.3 , pp. 1336-1345
    • Chen, F.L.1    Ou, T.Y.2
  • 107
    • 77958473967 scopus 로고    scopus 로고
    • Incremental-based extreme learning machine algorithms for time-variant neural networks
    • Ye Y, Squartim S, Piazza F (2010) Incremental-based extreme learning machine algorithms for time-variant neural networks. Lect Notes Comput Sci 6215: 9-16.
    • (2010) Lect Notes Comput Sci , vol.6215 , pp. 9-16
    • Ye, Y.1    Squartim, S.2    Piazza, F.3
  • 108
    • 77957870624 scopus 로고    scopus 로고
    • Performance enhancement of extreme learning machine for multi-category sparse data classification problems
    • 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): 1149-1157.
    • (2010) Eng Appl Artif Intell , vol.23 , Issue.7 , pp. 1149-1157
    • Suresh, S.1    Saraswathi, S.2    Sundararajan, N.3
  • 109
    • 77955430431 scopus 로고    scopus 로고
    • A new online learning algorithm for structure-adjustable extreme learning machine
    • Li G, Liu M, Dong M (2010) A new online learning algorithm for structure-adjustable extreme learning machine. Comput Math Appl 60(3): 377-389.
    • (2010) Comput Math Appl , vol.60 , Issue.3 , pp. 377-389
    • Li, G.1    Liu, M.2    Dong, M.3
  • 111
    • 77955204500 scopus 로고    scopus 로고
    • Color image watermarking using regularized extreme learning machine
    • Deng W, Chen L (2010) Color image watermarking using regularized extreme learning machine. Neural Network World 20(3): 317-330.
    • (2010) Neural Network World , vol.20 , Issue.3 , pp. 317-330
    • Deng, W.1    Chen, L.2
  • 112
    • 77955386853 scopus 로고    scopus 로고
    • Application of wave atoms decomposition and extreme learning machine for fingerprint classification
    • 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: 246-256.
    • (2010) Lect Notes Comput Sci , vol.6112 , pp. 246-256
    • Mohammed, A.A.1    Wu, Q.M.J.2    Sid-Ahmed, M.A.3
  • 113
    • 77952583382 scopus 로고    scopus 로고
    • Human action recognition using extreme learning machine based on visual vocabularies
    • Minhas R, Baradarani A, Seifzadeh S, Wu QMJ (2010) Human action recognition using extreme learning machine based on visual vocabularies. Neurocomputing 73: 1906-1917.
    • (2010) Neurocomputing , vol.73 , pp. 1906-1917
    • Minhas, R.1    Baradarani, A.2    Seifzadeh, S.3    Wu, Q.M.J.4
  • 114
    • 77952551629 scopus 로고    scopus 로고
    • Intelligent approaches using support vector machine and extreme learning machine for transmission line protection
    • Malathi V, Marimuthu NS, Baskar S (2010) Intelligent approaches using support vector machine and extreme learning machine for transmission line protection. Neurocomputing 73: 2160-2167.
    • (2010) Neurocomputing , vol.73 , pp. 2160-2167
    • Malathi, V.1    Marimuthu, N.S.2    Baskar, S.3
  • 115
    • 77953544300 scopus 로고    scopus 로고
    • Ternary reversible extreme learning machines: the incremental tri-training method for semi-supervised classification
    • 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): 345-372.
    • (2010) Knowl Inf Syst , vol.22 , Issue.3 , pp. 345-372
    • Tang, X.L.1    Han, M.2
  • 116
    • 49249135950 scopus 로고    scopus 로고
    • Power utility nontechnical loss analysis with extreme learning machine method
    • Nizar AH, Dong ZY, Wang Y (2008) Power utility nontechnical loss analysis with extreme learning machine method. IEEE Trans Power Syst 23(3): 946-955.
    • (2008) IEEE Trans Power Syst , vol.23 , Issue.3 , pp. 946-955
    • Nizar, A.H.1    Dong, Z.Y.2    Wang, Y.3


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.