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Volumn 367-368, Issue , 2016, Pages 1094-1105

A comprehensive evaluation of random vector functional link networks

Author keywords

Data classification; Moore Penrose pseudoinverse; Random vector functional link networks; Ridge regression

Indexed keywords

CHEMICAL ACTIVATION; RANDOM PROCESSES; REGRESSION ANALYSIS; TIME VARYING NETWORKS;

EID: 84951823694     PISSN: 00200255     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.ins.2015.09.025     Document Type: Article
Times cited : (375)

References (39)
  • 1
    • 84894439375 scopus 로고    scopus 로고
    • Fast decorrelated neural network ensembles with random weights
    • [1] Alhamdoosh, M., Wang, D., Fast decorrelated neural network ensembles with random weights. Inf. Sci. 264 (2014), 104–117.
    • (2014) Inf. Sci. , vol.264 , pp. 104-117
    • Alhamdoosh, M.1    Wang, D.2
  • 3
    • 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
    • [3] Bartlett, P.L., 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. Theor. 44:2 (1998), 525–536.
    • (1998) IEEE Trans. Inf. Theor. , vol.44 , Issue.2 , pp. 525-536
    • Bartlett, P.L.1
  • 4
    • 0346786584 scopus 로고    scopus 로고
    • Arcing classifier (with discussion and a rejoinder by the author)
    • [4] Breiman, L., Arcing classifier (with discussion and a rejoinder by the author). The Ann. Stat. 26:3 (1998), 801–849.
    • (1998) The Ann. Stat. , vol.26 , Issue.3 , pp. 801-849
    • Breiman, L.1
  • 5
    • 0033078284 scopus 로고    scopus 로고
    • A rapid learning and dynamic stepwise updating algorithm for flat neural networks and the application to time-series prediction
    • [5] Chen, C.P., Wan, J.Z., A rapid learning and dynamic stepwise updating algorithm for flat neural networks and the application to time-series prediction. IEEE Trans. Syst. Man Cybern. Part B: Cybern. 29:1 (1999), 62–72.
    • (1999) IEEE Trans. Syst. Man Cybern. Part B: Cybern. , vol.29 , Issue.1 , pp. 62-72
    • Chen, C.P.1    Wan, J.Z.2
  • 6
    • 24944471724 scopus 로고    scopus 로고
    • A statistical self-organizing learning system for remote sensing classification
    • [6] Chi, H.-M., Ersoy, O.K., A statistical self-organizing learning system for remote sensing classification. IEEE Trans. Geosci. Remote Sens. 43:8 (2005), 1890–1900.
    • (2005) IEEE Trans. Geosci. Remote Sens. , vol.43 , Issue.8 , pp. 1890-1900
    • Chi, H.-M.1    Ersoy, O.K.2
  • 8
    • 80054727848 scopus 로고    scopus 로고
    • A comprehensive survey on functional link neural networks and an adaptive pso–bp learning for cflnn
    • [8] Dehuri, S., Cho, S.-B., A comprehensive survey on functional link neural networks and an adaptive pso–bp learning for cflnn. Neural Comput. Appl. 19:2 (2010), 187–205.
    • (2010) Neural Comput. Appl. , vol.19 , Issue.2 , pp. 187-205
    • Dehuri, S.1    Cho, S.-B.2
  • 9
    • 29644438050 scopus 로고    scopus 로고
    • Statistical comparisons of classifiers over multiple data sets
    • [9] Demšar, J., Statistical comparisons of classifiers over multiple data sets. J. Mach. Learn. Res. 7 (2006), 1–30.
    • (2006) J. Mach. Learn. Res. , vol.7 , pp. 1-30
    • Demšar, J.1
  • 11
    • 84919773193 scopus 로고    scopus 로고
    • Do we need hundreds of classifiers to solve real world classification problems?
    • [11] Fernández-Delgado, M., Cernadas, E., Barro, S., Amorim, D., Do we need hundreds of classifiers to solve real world classification problems?. J. Mach. Learn. Res. 15:1 (2014), 3133–3181.
    • (2014) J. Mach. Learn. Res. , vol.15 , Issue.1 , pp. 3133-3181
    • Fernández-Delgado, M.1    Cernadas, E.2    Barro, S.3    Amorim, D.4
  • 12
    • 84944811700 scopus 로고
    • The use of ranks to avoid the assumption of normality implicit in the analysis of variance
    • [12] Friedman, M., The use of ranks to avoid the assumption of normality implicit in the analysis of variance. J. American Stat. Assoc. 32:200 (1937), 675–701.
    • (1937) J. American Stat. Assoc. , vol.32 , Issue.200 , pp. 675-701
    • Friedman, M.1
  • 13
    • 0001837148 scopus 로고
    • A comparison of alternative tests of significance for the problem of m rankings
    • [13] Friedman, M., A comparison of alternative tests of significance for the problem of m rankings. Ann. Math. Stat. 11:1 (1940), 86–92.
    • (1940) Ann. Math. Stat. , vol.11 , Issue.1 , pp. 86-92
    • Friedman, M.1
  • 14
    • 0024880831 scopus 로고
    • Multilayer feedforward networks are universal approximators
    • [14] Hornik, K., Stinchcombe, M., White, H., Multilayer feedforward networks are universal approximators. Neural Netw. 2:5 (1989), 359–366.
    • (1989) Neural Netw. , vol.2 , Issue.5 , pp. 359-366
    • Hornik, K.1    Stinchcombe, M.2    White, H.3
  • 15
    • 49649091922 scopus 로고    scopus 로고
    • Modelling conditional probabilities with committees of RVFL networks
    • Springer
    • [15] Husmeier, D., Taylor, J.G., Modelling conditional probabilities with committees of RVFL networks. Artificial Neural Networks–ICANN’97, 1997, Springer, 1053–1058.
    • (1997) Artificial Neural Networks–ICANN’97 , pp. 1053-1058
    • Husmeier, D.1    Taylor, J.G.2
  • 16
    • 0031913824 scopus 로고    scopus 로고
    • Neural networks for predicting conditional probability densities: improved training scheme combining EM and RVFL
    • [16] Husmeier, D., Taylor, J.G., Neural networks for predicting conditional probability densities: improved training scheme combining EM and RVFL. Neural Netw. 11:1 (1998), 89–116.
    • (1998) Neural Netw. , vol.11 , Issue.1 , pp. 89-116
    • Husmeier, D.1    Taylor, J.G.2
  • 17
    • 0029403793 scopus 로고
    • Stochastic choice of basis functions in adaptive function approximation and the functional-link net
    • [17] Igelnik, B., Pao, Y.-H., Stochastic choice of basis functions in adaptive function approximation and the functional-link net. IEEE Trans. Neural Netw. 6:6 (1995), 1320–1329.
    • (1995) IEEE Trans. Neural Netw. , vol.6 , Issue.6 , pp. 1320-1329
    • Igelnik, B.1    Pao, Y.-H.2
  • 20
    • 0027262895 scopus 로고
    • Multilayer feedforward networks with a nonpolynomial activation function can approximate any function
    • [20] Leshno, M., Lin, V.Y., Pinkus, A., Schocken, S., Multilayer feedforward networks with a nonpolynomial activation function can approximate any function. Neural Netw. 6:6 (1993), 861–867.
    • (1993) Neural Netw. , vol.6 , Issue.6 , pp. 861-867
    • Leshno, M.1    Lin, V.Y.2    Pinkus, A.3    Schocken, S.4
  • 21
    • 84921350514 scopus 로고    scopus 로고
    • Multisource data ensemble modeling for clinker free lime content estimate in rotary kiln sintering processes
    • [21] Li, W., Wang, D., Chai, T., Multisource data ensemble modeling for clinker free lime content estimate in rotary kiln sintering processes. IEEE Trans. Syst. Man Cybern.: Syst. 45:2 (2015), 303–314, 10.1109/TSMC.2014.2332305.
    • (2015) IEEE Trans. Syst. Man Cybern.: Syst. , vol.45 , Issue.2 , pp. 303-314
    • Li, W.1    Wang, D.2    Chai, T.3
  • 23
    • 84904207987 scopus 로고    scopus 로고
    • Hardware implementation methods in random vector functional-link networks
    • [23] Martínez-Villena, J.M., Rosado-Muñoz, A., Soria-Olivas, E., Hardware implementation methods in random vector functional-link networks. Appl. Intell. 41:1 (2014), 184–195.
    • (2014) Appl. Intell. , vol.41 , Issue.1 , pp. 184-195
    • Martínez-Villena, J.M.1    Rosado-Muñoz, A.2    Soria-Olivas, E.3
  • 26
    • 0028420218 scopus 로고
    • Learning and generalization characteristics of the random vector functional-link net
    • [26] Pao, Y.-H., Park, G.-H., Sobajic, D.J., Learning and generalization characteristics of the random vector functional-link net. Neurocomputing 6:2 (1994), 163–180.
    • (1994) Neurocomputing , vol.6 , Issue.2 , pp. 163-180
    • Pao, Y.-H.1    Park, G.-H.2    Sobajic, D.J.3
  • 27
    • 0029379686 scopus 로고
    • The functional link net and learning optimal control
    • [27] Pao, Y.-H., Phillips, S.M., The functional link net and learning optimal control. Neurocomputing 9:2 (1995), 149–164.
    • (1995) Neurocomputing , vol.9 , Issue.2 , pp. 149-164
    • Pao, Y.-H.1    Phillips, S.M.2
  • 28
    • 0003049327 scopus 로고
    • Neural-net computing and the intelligent control of systems
    • [28] Pao, Y.-H., Phillips, S.M., Sobajic, D.J., Neural-net computing and the intelligent control of systems. Int. J. Control 56:2 (1992), 263–289.
    • (1992) Int. J. Control , vol.56 , Issue.2 , pp. 263-289
    • Pao, Y.-H.1    Phillips, S.M.2    Sobajic, D.J.3
  • 29
    • 0034298399 scopus 로고    scopus 로고
    • Intelligent rate control for MPEG-4 coders
    • [29] Park, G.H., Lee, Y.J., LeClair, S.R., Intelligent rate control for MPEG-4 coders. Eng. Appl. Artif. Intell. 13:5 (2000), 565–575.
    • (2000) Eng. Appl. Artif. Intell. , vol.13 , Issue.5 , pp. 565-575
    • Park, G.H.1    Lee, Y.J.2    LeClair, S.R.3
  • 30
    • 0342980889 scopus 로고    scopus 로고
    • Unconstrained word-based approach for off-line script recognition using density-based random-vector functional-link net
    • [30] Park, G.H., Pao, Y.H., Unconstrained word-based approach for off-line script recognition using density-based random-vector functional-link net. Neurocomputing 31:1 (2000), 45–65.
    • (2000) Neurocomputing , vol.31 , Issue.1 , pp. 45-65
    • Park, G.H.1    Pao, Y.H.2
  • 31
    • 0000106040 scopus 로고
    • Universal approximation using radial-basis-function networks
    • [31] Park, J., Sandberg, I.W., Universal approximation using radial-basis-function networks. Neural Comput. 3:2 (1991), 246–257.
    • (1991) Neural Comput. , vol.3 , Issue.2 , pp. 246-257
    • Park, J.1    Sandberg, I.W.2
  • 34
    • 84922746025 scopus 로고    scopus 로고
    • Distributed learning for random vector functional-link networks
    • [34] Scardapane, S., Wang, D., Panella, M., Uncini, A., Distributed learning for random vector functional-link networks. Inf. Sci. 301 (2015), 271–284.
    • (2015) Inf. Sci. , vol.301 , pp. 271-284
    • Scardapane, S.1    Wang, D.2    Panella, M.3    Uncini, A.4
  • 39
    • 84902189441 scopus 로고    scopus 로고
    • A high accuracy pedestrian detection system combining a cascade adaboost detector and random vector functional-link net
    • [39] Wang, Z., Yoon, S., Xie, S.J., Lu, Y., Park, D.S., A high accuracy pedestrian detection system combining a cascade adaboost detector and random vector functional-link net. The Scientific World J., 2014, 2014.
    • (2014) The Scientific World J. , vol.2014
    • Wang, Z.1    Yoon, S.2    Xie, S.J.3    Lu, Y.4    Park, D.S.5


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.