메뉴 건너뛰기




Volumn , Issue , 2009, Pages 60-94

Learning algorithms for RBF functions and subspace based functions

Author keywords

[No Author keywords available]

Indexed keywords


EID: 77956476076     PISSN: None     EISSN: None     Source Type: Book    
DOI: 10.4018/978-1-60566-766-9.ch003     Document Type: Chapter
Times cited : (11)

References (83)
  • 1
    • 0016355478 scopus 로고
    • A new look at the statistical model identification
    • Akaike, H. (1974). A new look at the statistical model identification. IEEE Transactions on Automatic Control, 19, 714-723.
    • (1974) IEEE Transactions on Automatic Control , vol.19 , pp. 714-723
    • Akaike, H.1
  • 2
    • 21144438166 scopus 로고
    • Likelihood of a model and information criteria
    • Akaike, H. (1981). Likelihood of a model and information criteria. Journal of Econometrics, 16, 3-14.
    • (1981) Journal of Econometrics , vol.16 , pp. 3-14
    • Akaike, H.1
  • 3
    • 33749754615 scopus 로고    scopus 로고
    • A new learning algorithm for blind separation of sources
    • In Touretzky, Mozer, & Hasselmo (Eds.), MIT Press
    • Amari, S. I., Cichocki, A., & Yang, H. (1996), A new learning algorithm for blind separation of sources, In Touretzky, Mozer, & Hasselmo (Eds.), Advances in Neural Information Processing System 8, MIT Press, 757-763.
    • (1996) Advances in Neural Information Processing System , vol.8 , pp. 757-763
    • Amari, S.I.1    Cichocki, A.2    Yang, H.3
  • 4
    • 0029411030 scopus 로고
    • An information-maximization approach to blind separation and blind deconvolution
    • Bell, A., & Sejnowski, T. (1995). An information-maximization approach to blind separation and blind deconvolution. Neural Computation, 7, 1129-1159.
    • (1995) Neural Computation , vol.7 , pp. 1129-1159
    • Bell, A.1    Sejnowski, T.2
  • 5
    • 0008995203 scopus 로고
    • Generalization properties of radial basis function
    • In Lippmann, Moody, & Touretzky (eds), Morgan Kaufmann Pub
    • Botros, S. M., & Atkeson, C. G. (1991), Generalization properties of radial basis function, In Lippmann, Moody, & Touretzky (eds), Advances in Neural Information Processing System 3, Morgan Kaufmann Pub., 707-713.
    • (1991) Advances in Neural Information Processing System 3 , pp. 707-713
    • Botros, S.M.1    Atkeson, C.G.2
  • 6
    • 34250108028 scopus 로고
    • Model selection and Akaike's information criterion: The general theory and its analytical extension
    • Bozdogan, H. (1987). Model Selection and Akaike's Information Criterion: The general theory and its analytical extension. Psychometrika, 52, 345-370.
    • (1987) Psychometrika , vol.52 , pp. 345-370
    • Bozdogan, H.1
  • 7
    • 0000621802 scopus 로고
    • Multivariable functional interpolation and adaptive networks
    • Broomhead, D. S., & Lowe, D. (1988). Multivariable functional interpolation and adaptive networks. Complex Systems, 2, 321-323.
    • (1988) Complex Systems , vol.2 , pp. 321-323
    • Broomhead, D.S.1    Lowe, D.2
  • 8
    • 0031591140 scopus 로고    scopus 로고
    • Unifying the derivations for the akaike and corrected akaike information criteria
    • Cavanaugh, J. (1997). Unifying the derivations for the Akaike and corrected Akaike information criteria. Statistics & Probability Letters, 33, 201-208.
    • (1997) Statistics & Probability Letters , vol.33 , pp. 201-208
    • Cavanaugh, J.1
  • 9
    • 0031076555 scopus 로고    scopus 로고
    • Environment-adaptation mobile radio propagation prediction using radial basis function neural networks
    • Chang, P. R., & Yang, W. H. (1997). Environment-adaptation mobile radio propagation prediction using radial basis function neural networks. IEEE Transactions on Vehicular Technology, 46, 155-160.
    • (1997) IEEE Transactions on Vehicular Technology , vol.46 , pp. 155-160
    • Chang, P.R.1    Yang, W.H.2
  • 10
    • 0026116468 scopus 로고
    • Orthogonal least squares learning algorithm for radial basis function networks
    • Chen, S., Cowan, C. N., & Grant, P. M. (1991). Orthogonal least squares learning algorithm for Radial basis function networks. IEEE Transactions on Neural Networks, 2, 302-309.
    • (1991) IEEE Transactions on Neural Networks , vol.2 , pp. 302-309
    • Chen, S.1    Cowan, C.N.2    Grant, P.M.3
  • 12
    • 0001244757 scopus 로고
    • On the almost everywhere convergence of nonparametric regression function estimates
    • Devroye, L. (1981). On the almost everywhere convergence of nonparametric regression function estimates. Annals of Statistics, 9, 1310-1319.
    • (1981) Annals of Statistics , vol.9 , pp. 1310-1319
    • Devroye, L.1
  • 14
    • 0036565011 scopus 로고    scopus 로고
    • Face recognition with radial basis function (RBF) neural networks
    • Er, M. J. (2002). Face recognition with radial basis function (RBF) neural networks. IEEE Transactions on Neural Networks, 13(3), 697-710.
    • (2002) IEEE Transactions on Neural Networks , vol.13 , Issue.3 , pp. 697-710
    • Er, M.J.1
  • 16
    • 84898934543 scopus 로고    scopus 로고
    • Variational inference for bayesian mixtures of factor analysers
    • In Solla, Leen, & Muller (eds), MIT Press
    • Ghahramani, Z., & Beal, M. J. (2000). Variational inference for Bayesian mixtures of factor analysers. In Solla, Leen, & Muller (eds), Advances in Neural Information Processing Systems 12, MIT Press, 449-455.
    • (2000) Advances in Neural Information Processing Systems 12 , pp. 449-455
    • Ghahramani, Z.1    Beal, M.J.2
  • 17
    • 0024991997 scopus 로고
    • Networks and the best approximation property
    • Girosi, F., & Poggio, T. (1990). Networks and the best approximation property. Biological Cybernetics, 63(3), 169-176.
    • (1990) Biological Cybernetics , vol.63 , Issue.3 , pp. 169-176
    • Girosi, F.1    Poggio, T.2
  • 18
    • 35248838920 scopus 로고    scopus 로고
    • Multi-step ahead nonlinear identification of Lorenz's chaotic system using radial basis neural network with learning by clustering and particle swarm optimization
    • Guerra, F. A., & Coelho, L. S. (2008). Multi-step ahead nonlinear identification of Lorenz's chaotic system using radial basis neural network with learning by clustering and particle swarm optimization. Chaos, Solitons, and Fractals, 35(5), 967-979.
    • (2008) Chaos, Solitons, and Fractals , vol.35 , Issue.5 , pp. 967-979
    • Guerra, F.A.1    Coelho, L.S.2
  • 19
    • 0001683814 scopus 로고
    • Layered neural networks with gaussian hidden units as universal approximations
    • Hartman, E. J., Keeler, J. D., & Kowalski, J. M. (1990). Layered neural networks with Gaussian hidden units as universal approximations. Neural Computation, 2, 210-215.
    • (1990) Neural Computation , vol.2 , pp. 210-215
    • Hartman, E.J.1    Keeler, J.D.2    Kowalski, J.M.3
  • 21
    • 0029652445 scopus 로고
    • The wake-sleep algorithm for unsupervised learning neural networks
    • Hinton, G. E., Dayan, P., Frey, B. J., & Neal, R. N. (1995). The wake-sleep algorithm for unsupervised learning neural networks. Science, 268, 1158-1160.
    • (1995) Science , vol.268 , pp. 1158-1160
    • Hinton, G.E.1    Dayan, P.2    Frey, B.J.3    Neal, R.N.4
  • 22
    • 0002834189 scopus 로고
    • Autoencoders, minimum description length and helmholtz free energy
    • In Cowan, Tesauro, & Alspector (eds), Morgan Kaufmann Pub
    • Hinton, G. E., & Zemel, R. S. (1994), Autoencoders, minimum description length and Helmholtz free energy, In Cowan, Tesauro, & Alspector (eds), Advances in Neural Information Processing Systems 6, Morgan Kaufmann Pub., 449-455.
    • (1994) Advances in Neural Information Processing Systems 6 , pp. 449-455
    • Hinton, G.E.1    Zemel, R.S.2
  • 23
    • 28144453046 scopus 로고    scopus 로고
    • Wide-band dynamic modeling of power amplifiers using radial-basis function neural networks
    • Isaksson, M., Wisell, D., & Ronnow, D. (2005). Wide-band dynamic modeling of power amplifiers using radial-basis function neural networks. IEEE Transactions on Microwave Theory and Techniques, 53(11), 3422-3428.
    • (2005) IEEE Transactions on Microwave Theory and Techniques , vol.53 , Issue.11 , pp. 3422-3428
    • Isaksson, M.1    Wisell, D.2    Ronnow, D.3
  • 24
    • 4043084564 scopus 로고    scopus 로고
    • Tutorial on variational approximation methods
    • in Opper & Saad (eds), MIT press
    • Jaakkola, T. S. (2001), Tutorial on variational approximation methods, in Opper & Saad (eds), Advanced Mean Field Methods: Theory and Practice, MIT press, 129-160.
    • (2001) Advanced Mean Field Methods: Theory and Practice , pp. 129-160
    • Jaakkola, T.S.1
  • 26
    • 0033225865 scopus 로고    scopus 로고
    • Introduction to variational methods for graphical models
    • Jordan, M., Ghahramani, Z., Jaakkola, T., & Saul, L. (1999). Introduction to variational methods for graphical models. Machine Learning, 37, 183-233.
    • (1999) Machine Learning , vol.37 , pp. 183-233
    • Jordan, M.1    Ghahramani, Z.2    Jaakkola, T.3    Saul, L.4
  • 27
    • 0000262562 scopus 로고
    • Hierarchical mixtures of experts and the EM algorithm
    • Jordan, M. I., & Jacobs, R. A. (1994). Hierarchical mixtures of experts and the EM algorithm. Neural Computation, 6, 181-214.
    • (1994) Neural Computation , vol.6 , pp. 181-214
    • Jordan, M.I.1    Jacobs, R.A.2
  • 28
    • 0029617280 scopus 로고
    • Convergence Results for The EM Approach to Mixtures of Experts Architectures
    • Jordan, M. I., & Xu, L. (1995). Convergence Results for The EM Approach to Mixtures of Experts Architectures. Neural Networks, 8, 1409-1431.
    • (1995) Neural Networks , vol.8 , pp. 1409-1431
    • Jordan, M.I.1    Xu, L.2
  • 30
    • 0011180615 scopus 로고
    • Sequential adaptation of radial basis function neural networks and its application to time-series prediction
    • In Lippmann, Moody, & Touretzky (eds), Morgan Kaufmann Pub
    • Kardirkamanathan, V., Niranjan, M., & Fallside, F. (1991), Sequential adaptation of Radial basis function neural networks and its application to time-series prediction, In Lippmann, Moody, & Touretzky (eds), Advances in Neural Information Processing System 3, Morgan Kaufmann Pub., 721-727.
    • (1991) Advances in Neural Information Processing System 3 , pp. 721-727
    • Kardirkamanathan, V.1    Niranjan, M.2    Fallside, F.3
  • 31
    • 3242882795 scopus 로고    scopus 로고
    • Bayesian information criteria and smoothing parameter selection in radial basis function networks
    • Konishi, S., Ando, T., & Imoto, S. (2004). Bayesian information criteria and smoothing parameter selection in radial basis function networks. Biometrika, 91(1), 27-43.
    • (2004) Biometrika , vol.91 , Issue.1 , pp. 27-43
    • Konishi, S.1    Ando, T.2    Imoto, S.3
  • 32
    • 0033097804 scopus 로고    scopus 로고
    • A practical radial basis function equalizer
    • Lee, J. (1999). A practical radial basis function equalizer. IEEE Transactions on Neural Networks, 10, 450-455.
    • (1999) IEEE Transactions on Neural Networks , vol.10 , pp. 450-455
    • Lee, J.1
  • 33
    • 1642387025 scopus 로고    scopus 로고
    • A non-linear rainfall-runoff model using radial basis function network
    • (Amsterdam)
    • Lin, G. F., & Chen, L. H. (2004). A non-linear rainfall-runoff model using radial basis function network. Journal of Hydrology (Amsterdam), 289, 1-8.
    • (2004) Journal of Hydrology , vol.289 , pp. 1-8
    • Lin, G.F.1    Chen, L.H.2
  • 34
    • 0031105693 scopus 로고    scopus 로고
    • An efficient EM-based training algorithm for feedforward neural networks
    • Ma, S., Ji, C., & Farmer, J. (1997). An Efficient EM-based Training Algorithm for Feedforward Neural Networks. Neural Networks, 10(2), 243-256.
    • (1997) Neural Networks , vol.10 , Issue.2 , pp. 243-256
    • Ma, S.1    Ji, C.2    Farmer, J.3
  • 36
    • 0002704818 scopus 로고
    • A practical bayesian framework for backpropagation
    • Mackey, D. (1992). A practical Bayesian framework for backpropagation. Neural Computation, 4, 448-472.
    • (1992) Neural Computation , vol.4 , pp. 448-472
    • Mackey, D.1
  • 37
    • 0035137045 scopus 로고    scopus 로고
    • Numerical solution of differential equations using multiquadric radial basis function networks
    • Mai-Duy, N., & Tran-Cong, T. (2001). Numerical solution of differential equations using multiquadric radial basis function networks. Neural Networks, 14(2), 185-199.
    • (2001) Neural Networks , vol.14 , Issue.2 , pp. 185-199
    • Mai-Duy, N.1    Tran-Cong, T.2
  • 39
    • 0342356992 scopus 로고
    • How receptive field parameters affect neural learning
    • In Lippmann, Moody, & Touretzky (eds), Morgan Kaufmann Pub
    • Mel, B. W., & Omohundro, S. M. (1991), How receptive field parameters affect neural learning. In Lippmann, Moody, & Touretzky (eds), Advances in Neural Information Processing System 3, Morgan Kaufmann Pub., 757-763.
    • (1991) Advances in Neural Information Processing System 3 , pp. 757-763
    • Mel, B.W.1    Omohundro, S.M.2
  • 40
    • 0000672424 scopus 로고
    • Fast learning in networks of locally-tuned processing units
    • Moody, J., & Darken, C. (1989). Fast learning in networks of locally-tuned processing units. Neural Computation, 1, 281-294.
    • (1989) Neural Computation , vol.1 , pp. 281-294
    • Moody, J.1    Darken, C.2
  • 41
    • 0031084370 scopus 로고    scopus 로고
    • Regression and time series model selection using variants of the schwarz information criterion
    • Neath, A. A., & Cavanaugh, J. E. (1997). Regression and Time Series model selection using variants of the Schwarz information criterion. Communications in Statistics A, 26, 559-580.
    • (1997) Communications in Statistics A , vol.26 , pp. 559-580
    • Neath, A.A.1    Cavanaugh, J.E.2
  • 43
    • 0001002401 scopus 로고
    • Universal approximation using radial-basis-function networks
    • Park, J., & Sandberg, I. W. (1993). Universal approximation using radial-basis-function networks. Neural Computation, 5, 305-316.
    • (1993) Neural Computation , vol.5 , pp. 305-316
    • Park, J.1    Sandberg, I.W.2
  • 44
    • 0025490985 scopus 로고
    • Networks for approximation and learning, networks for approximation and learning
    • Poggio, T., & Girosi, F. (1990). Networks for approximation and learning, Networks for approximation and learning. Proceedings of the IEEE, 78, 1481-1497.
    • (1990) Proceedings of the IEEE , vol.78 , pp. 1481-1497
    • Poggio, T.1    Girosi, F.2
  • 46
    • 0037392656 scopus 로고    scopus 로고
    • Structural damage detection in a helicopter rotor blade using radial basis function neural networks
    • Reddy, R., & Ganguli, R. (2003). Structural damage detection in a helicopter rotor blade using radial basis function neural networks. Smart Materials and Structures, 12, 232-241.
    • (2003) Smart Materials and Structures , vol.12 , pp. 232-241
    • Reddy, R.1    Ganguli, R.2
  • 47
    • 0021404166 scopus 로고
    • Mixture densities, maximum likelihood, and the EM algorithm
    • Redner, R. A., & Walker, H. F. (1984). Mixture densities, maximum likelihood, and the EM algorithm. SIAM Review, 26, 195-239.
    • (1984) SIAM Review , vol.26 , pp. 195-239
    • Redner, R.A.1    Walker, H.F.2
  • 48
    • 0000318553 scopus 로고
    • Stochastic complexity and modeling
    • Rissanen, J. (1986). Stochastic complexity and modeling. Annals of Statistics, 14(3), 1080-1100.
    • (1986) Annals of Statistics , vol.14 , Issue.3 , pp. 1080-1100
    • Rissanen, J.1
  • 51
    • 0022471098 scopus 로고
    • Learning representations by back-propagating errors
    • Rumelhart, D. E., Hintont, G. E., & Williams, R. J. (1986). Learning representations by back-propagating errors. Nature, 323, 533-536.
    • (1986) Nature , vol.323 , pp. 533-536
    • Rumelhart, D.E.1    Hintont, G.E.2    Williams, R.J.3
  • 53
    • 29144461068 scopus 로고    scopus 로고
    • Classification technique based on radial basis function neural networks
    • Sarimveis, H., Doganis, P., & Alexandridis, A. (2006). classification technique based on radial basis function neural networks. Advances in Engineering Software, 37(4), 218-221.
    • (2006) Advances in Engineering Software , vol.37 , Issue.4 , pp. 218-221
    • Sarimveis, H.1    Doganis, P.2    Alexandridis, A.3
  • 54
    • 0000120766 scopus 로고
    • Estimating the dimension of a model
    • Schwarz, G. (1978). Estimating the dimension of a model. Annals of Statistics, 6, 461-464.
    • (1978) Annals of Statistics , vol.6 , pp. 461-464
    • Schwarz, G.1
  • 55
    • 51749109145 scopus 로고    scopus 로고
    • Bayesian ying-yang harmony learning for local factor analysis: A comparative investigation
    • In Tizhoosh & Ventresca (eds), SpringerVerlag
    • Shi, L. (2008), Bayesian Ying-Yang harmony learning for local factor analysis: a comparative investigation, In Tizhoosh & Ventresca (eds), Oppositional Concepts in Computational Intelligence, SpringerVerlag, 209-232.
    • (2008) Oppositional Concepts in Computational Intelligence , pp. 209-232
    • Shi, L.1
  • 56
    • 0025206332 scopus 로고
    • Probabilistic neural networks
    • Specht, D. F. (1990). Probabilistic neural networks. Neural Networks, 3, 109-118.
    • (1990) Neural Networks , vol.3 , pp. 109-118
    • Specht, D.F.1
  • 57
    • 0000319198 scopus 로고
    • Cross-validation: A review
    • Stone, M. (1978). Cross-validation: A review. Math. Operat. Statist., 9, 127-140.
    • (1978) Math. Operat. Statist , vol.9 , pp. 127-140
    • Stone, M.1
  • 63
    • 0000107517 scopus 로고
    • An information measure for classification
    • Wallace, C. S., & Boulton, D. M. (1968). An information measure for classification. The Computer Journal, 11, 185-194.
    • (1968) The Computer Journal , vol.11 , pp. 185-194
    • Wallace, C.S.1    Boulton, D.M.2
  • 65
    • 0344073962 scopus 로고    scopus 로고
    • RBF nets, mixture experts, and bayesian ying-yang learning
    • Xu, L. (1998). RBF nets, mixture experts, and Bayesian Ying-Yang learning. Neurocomputing, 19(1-3), 223-257.
    • (1998) Neurocomputing , vol.19 , Issue.1-3 , pp. 223-257
    • Xu, L.1
  • 66
    • 0035259214 scopus 로고    scopus 로고
    • Best harmony, unified RPCL and automated model selection for unsupervised and supervised learning on gaussian mixtures, ME-RBF models and three-layer nets
    • Xu, L. (2001). Best Harmony, Unified RPCL and Automated Model Selection for Unsupervised and Supervised Learning on Gaussian Mixtures, ME-RBF Models and Three-Layer Nets. International Journal of Neural Systems, 11(1), 3-69.
    • (2001) International Journal of Neural Systems , vol.11 , Issue.1 , pp. 3-69
    • Xu, L.1
  • 67
    • 0035391741 scopus 로고    scopus 로고
    • BYY harmony learning, independent state space and generalized APT financial analyses
    • Xu, L. (2001). BYY harmony learning, independent state space and generalized APT financial analyses. IEEE Transactions on Neural Networks, 12(4), 822-849.
    • (2001) IEEE Transactions on Neural Networks , vol.12 , Issue.4 , pp. 822-849
    • Xu, L.1
  • 68
    • 0036790879 scopus 로고    scopus 로고
    • BYY harmony learning, structural RPCL, and topological self-organizing on unsupervised and supervised mixture models
    • Xu, L. (2002). BYY harmony learning, structural RPCL, and topological self-organizing on unsupervised and supervised mixture models. Neural Networks, 15, 1125-1151.
    • (2002) Neural Networks , vol.15 , pp. 1125-1151
    • Xu, L.1
  • 69
    • 3843136240 scopus 로고    scopus 로고
    • Advances on BYY harmony learning: Information theoretic perspective, generalized projection geometry, and independent factor auto-determination
    • Xu, L. (2004a). Advances on BYY harmony learning: information theoretic perspective, generalized projection geometry, and independent factor auto-determination. IEEE Transactions on Neural Networks, 15, 885-902.
    • (2004) IEEE Transactions on Neural Networks , vol.15 , pp. 885-902
    • Xu, L.1
  • 70
    • 3843066216 scopus 로고    scopus 로고
    • Temporal BYY encoding, markovian state spaces, and space dimension determination
    • Xu, L. (2004b). Temporal BYY encoding, Markovian state spaces, and space dimension determination. IEEE Transactions on Neural Networks, 15, 1276-1295.
    • (2004) IEEE Transactions on Neural Networks , vol.15 , pp. 1276-1295
    • Xu, L.1
  • 71
    • 33847155556 scopus 로고    scopus 로고
    • Fundamentals, challenges, and advances of statistical learning for knowledge discovery and problem solving: A BYY harmony perspective, keynote talk
    • Oct. 13-15, 2005, Beijing, China
    • Xu, L (2005), Fundamentals, Challenges, and Advances of Statistical Learning for Knowledge Discovery and Problem Solving: A BYY Harmony Perspective, Keynote talk. Proc. Of Intl. Conf. on Neural Networks and Brain, Oct. 13-15, 2005, Beijing, China, Vol. 1, 24-55.
    • (2005) Proc. of Intl. Conf. On Neural Networks and Brain , vol.1 , pp. 24-55
    • Xu, L.1
  • 72
    • 46049106446 scopus 로고    scopus 로고
    • Bayesian ying yang learning
    • 1809, Retrieved from
    • Xu, L. (2007a), Bayesian Ying Yang Learning, Scholarpedia 2(3):1809, Retrieved from http://scholar-pedia.org/article/Bayesian_Ying_Yang_learning.
    • (2007) Scholarpedia , vol.2 , Issue.3
    • Xu, L.1
  • 73
    • 46049083679 scopus 로고    scopus 로고
    • Rival penalized competitive learning
    • 1810, Retrieved from
    • Xu, L. (2007b), Rival penalized competitive learning, Scholarpedia 2(8):1810, Retrieved from http://www.scholarpedia.org/article/Rival_penalized_competitive_learning
    • (2007) Scholarpedia , vol.2 , Issue.8
    • Xu, L.1
  • 74
    • 34249982553 scopus 로고    scopus 로고
    • A trend on regularization and model selection in statistical learning: A bayesian ying yang learning perspective
    • In Duch & Mandziuk (eds.), Springer-Verlag
    • Xu, L. (2007c), A trend on regularization and model selection in statistical learning: a Bayesian Ying Yang learning perspective, In Duch & Mandziuk (eds.), Challenges for Computational Intelligence, Springer-Verlag, 365-406.
    • (2007) Challenges for Computational Intelligence , pp. 365-406
    • Xu, L.1
  • 75
    • 34247105235 scopus 로고    scopus 로고
    • A unified perspective and new results on RHT computing, mixture based learning, and multi-learner based problem solving
    • Xu, L. (2007d). A unified perspective and new results on RHT computing, mixture based learning, and multi-learner based problem solving. Pattern Recognition, 40, 2129-2153.
    • (2007) Pattern Recognition , vol.40 , pp. 2129-2153
    • Xu, L.1
  • 76
    • 46049101030 scopus 로고    scopus 로고
    • In Zurada et al (eds.), Computational Intelligence: Research Frontiers, WCCI2008 Plenary/ Invited Lectures, LNCS5050
    • Xu, L. (2008a), Bayesian Ying Yang System, Best Harmony Learning, and Gaussian Manifold Based Family, In Zurada et al (eds.) Computational Intelligence: Research Frontiers, WCCI2008 Plenary/ Invited Lectures, LNCS5050, 48-78.
    • (2008) Bayesian Ying Yang System, Best Harmony Learning, and Gaussian Manifold Based Family , pp. 48-78
    • Xu, L.1
  • 77
    • 67149106122 scopus 로고    scopus 로고
    • Machine learning problems from optimization perspective, a special issue for CDGO 07
    • (in press)
    • Xu, L. (2008b). (in press). Machine learning problems from optimization perspective, A special issue for CDGO 07. Journal of Global Optimization.
    • (2008) Journal of Global Optimization
    • Xu, L.1
  • 78
    • 77956473522 scopus 로고    scopus 로고
    • Independent subspaces
    • in Ramón, Dopico, Dorado & Pazos (Eds.), IGI Global (IGI) publishing company
    • Xu, L. (2008c), Independent Subspaces, in Ramón, Dopico, Dorado & Pazos (Eds.), Encyclopedia of Artificial Intelligence, IGI Global (IGI) publishing company, 903-912.
    • (2008) Encyclopedia of Artificial Intelligence , pp. 903-912
    • Xu, L.1
  • 80
    • 85140116568 scopus 로고
    • An alternative model for mixtures of experts
    • In Tesauro, Touretzky & Leen (eds), MIT Press
    • Xu, L., Jordan, M. I., & Hinton, G. E. (1995), An Alternative Model for Mixtures of Experts, In Tesauro, Touretzky & Leen (eds), Advances in Neural Information Processing Systems 7, MIT Press, 633-640.
    • (1995) Advances in Neural Information Processing Systems 7 , pp. 633-640
    • Xu, L.1    Jordan, M.I.2    Hinton, G.E.3
  • 81
    • 0027629412 scopus 로고
    • Rival penalized competitive learning for clustering analysis, RBF net and curve detection
    • An early version on Proc. 1992 IJCNN, Nov.3-6, 1992, Beijing
    • Xu, L., Krzyzak, A., & Oja, E. (1993). Rival Penalized Competitive Learning for Clustering Analysis, RBF net and Curve Detection. IEEE Trans. Neural Networks, 4(4), 636-649. An early version on Proc. 1992 IJCNN, Nov.3-6, 1992, Beijing, 665-670.
    • (1993) IEEE Trans. Neural Networks , vol.4 , Issue.4 , pp. 636-649
    • Xu, L.1    Krzyzak, A.2    Oja, E.3
  • 82
    • 0028341934 scopus 로고
    • On radial basis function nets and kernel regression: Statistical consistency, convergence rates and receptive field size
    • Xu, L., Krzyzak, A., & Yuille, A. L. (1994). On Radial Basis Function Nets and Kernel Regression: Statistical Consistency, Convergence Rates and Receptive Field Size. Neural Networks, 7(4), 609-628.
    • (1994) Neural Networks , vol.7 , Issue.4 , pp. 609-628
    • Xu, L.1    Krzyzak, A.2    Yuille, A.L.3


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.