Technology. His Current Research Interests In- Clude Neural Computation, Statistical Learning Theory, and Handwriting Recognition. Kwok and Yeung: Constructive Algorithms for Structure Learning \some Approximation Properties of Projection Pursuit Learning Networks," in Advances in Neural Information
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R. S. Schechter | Systems | J. Friedman | C. Atkeson | J. Moody | D. Rumelhart | T. Sejnowski | S. Hanson | J. Cowan | R. Lippmann | J. Keeler | D. Touretzky | B. Huberman | L. Pratt | Tin-Yau Kwok | G. E. Hinton | M. Maechler | C. L. Giles | M. Azimi-Sadjadi | S. Lay | S. Beveridge | Eng | H. Wechsler | B. Sc | S. Sheedvash | F. O. Trujillo | Kaufmann | R Setiono | L. Hui | J. Hwang | D. Martin | Y. Zhao | S. Springer | B. Workshop | S. Juang | C. Kung | Kamm | E Hartman | J. Kowalski | layered Neural | S J Hanson | Weigend | D. Touretzky
[1] M. Tummala,et al. Identification of Volterra systems with a polynomial neural network , 1992, [Proceedings] ICASSP-92: 1992 IEEE International Conference on Acoustics, Speech, and Signal Processing.
[2] Gábor Lugosi,et al. Nonparametric estimation via empirical risk minimization , 1995, IEEE Trans. Inf. Theory.
[3] Andrew R. Barron,et al. Universal approximation bounds for superpositions of a sigmoidal function , 1993, IEEE Trans. Inf. Theory.
[4] John Moody,et al. Prediction Risk and Architecture Selection for Neural Networks , 1994 .
[5] Jooyoung Park,et al. Universal Approximation Using Radial-Basis-Function Networks , 1991, Neural Computation.
[6] Russell Reed,et al. Pruning algorithms-a survey , 1993, IEEE Trans. Neural Networks.
[7] Jooyoung Park,et al. Approximation and Radial-Basis-Function Networks , 1993, Neural Computation.
[8] E. Fiesler,et al. Comparative Bibliography of Ontogenic Neural Networks , 1994 .
[9] James D. Keeler,et al. Predicting the Future: Advantages of Semilocal Units , 1991, Neural Computation.
[10] M. Golea,et al. A Convergence Theorem for Sequential Learning in Two-Layer Perceptrons , 1990 .
[11] G. Schwarz. Estimating the Dimension of a Model , 1978 .
[12] Ken-ichi Funahashi,et al. On the approximate realization of continuous mappings by neural networks , 1989, Neural Networks.
[13] Brian D. Ripley,et al. Statistical Ideas for Selecting Network Architectures , 1995, SNN Symposium on Neural Networks.
[14] H. Akaike. A new look at the statistical model identification , 1974 .
[15] Garrison W. Cottrell,et al. Topology-modifying neural network algorithms , 1998 .
[16] Leslie G. Valiant,et al. A theory of the learnable , 1984, CACM.
[17] Blake LeBaron,et al. Evaluating Neural Network Predictors by Bootstrapping , 1994 .
[18] James D. Keeler,et al. Layered Neural Networks with Gaussian Hidden Units as Universal Approximations , 1990, Neural Computation.
[19] Michael I. Jordan,et al. Advances in Neural Information Processing Systems 30 , 1995 .
[20] Kurt Hornik,et al. Some new results on neural network approximation , 1993, Neural Networks.
[21] R. A. Silverman,et al. Introductory Real Analysis , 1972 .
[22] John Moody,et al. Note on generalization, regularization and architecture selection in nonlinear learning systems , 1991, Neural Networks for Signal Processing Proceedings of the 1991 IEEE Workshop.
[23] Gail Gong. Cross-Validation, the Jackknife, and the Bootstrap: Excess Error Estimation in Forward Logistic Regression , 1986 .
[24] Manoel Fernando Tenorio,et al. Self-organizing network for optimum supervised learning , 1990, IEEE Trans. Neural Networks.
[25] George Cybenko,et al. Approximation by superpositions of a sigmoidal function , 1989, Math. Control. Signals Syst..
[26] S. K. Rogers,et al. A taxonomy of neural network optimality , 1992, Proceedings of the IEEE 1992 National Aerospace and Electronics Conference@m_NAECON 1992.
[27] 統計数理研究所. Annals of the institute of statistical mathematics , 1988, Public Choice.
[28] Elie Bienenstock,et al. Neural Networks and the Bias/Variance Dilemma , 1992, Neural Computation.
[29] Babak Hassibi,et al. Second Order Derivatives for Network Pruning: Optimal Brain Surgeon , 1992, NIPS.
[30] Michael C. Mozer,et al. Skeletonization: A Technique for Trimming the Fat from a Network via Relevance Assessment , 1988, NIPS.
[31] A. Barron. Approximation and Estimation Bounds for Artificial Neural Networks , 1991, COLT '91.
[32] Harry Wechsler,et al. From Statistics to Neural Networks: Theory and Pattern Recognition Applications , 1996 .
[33] Peter Craven,et al. Smoothing noisy data with spline functions , 1978 .
[34] Guillaume Deffuant. Neural units recruitment algorithm for generation of decision trees , 1990, 1990 IJCNN International Joint Conference on Neural Networks.
[35] M. Stone,et al. Cross‐Validatory Choice and Assessment of Statistical Predictions , 1976 .
[36] C. Jutten,et al. Gal: Networks That Grow When They Learn and Shrink When They Forget , 1991 .
[37] Ehud D. Karnin,et al. A simple procedure for pruning back-propagation trained neural networks , 1990, IEEE Trans. Neural Networks.
[38] Eric B. Baum,et al. A Proposal for More Powerful Learning Algorithms , 1989, Neural Computation.
[39] Kurt Hornik,et al. Approximation capabilities of multilayer feedforward networks , 1991, Neural Networks.
[40] J. Rissanen,et al. Modeling By Shortest Data Description* , 1978, Autom..
[41] Marcus Frean,et al. The Upstart Algorithm: A Method for Constructing and Training Feedforward Neural Networks , 1990, Neural Computation.
[42] Steve Renals. Radial basis function network for speech pattern classification , 1989 .