What Size Net Gives Valid Generalization?
暂无分享,去创建一个
[1] Thomas M. Cover,et al. Geometrical and Statistical Properties of Systems of Linear Inequalities with Applications in Pattern Recognition , 1965, IEEE Trans. Electron. Comput..
[2] Vladimir Vapnik,et al. Chervonenkis: On the uniform convergence of relative frequencies of events to their probabilities , 1971 .
[3] Norbert Sauer,et al. On the Density of Families of Sets , 1972, J. Comb. Theory, Ser. A.
[4] Richard O. Duda,et al. Pattern classification and scene analysis , 1974, A Wiley-Interscience publication.
[5] Temple F. Smith. Occam's razor , 1980, Nature.
[6] Richard M. Dudley,et al. Some special vapnik-chervonenkis classes , 1981, Discret. Math..
[7] D. Pollard. Convergence of stochastic processes , 1984 .
[8] Leslie G. Valiant,et al. A theory of the learnable , 1984, STOC '84.
[9] Lawrence D. Jackel,et al. Large Automatic Learning, Rule Extraction, and Generalization , 1987, Complex Syst..
[10] N. Littlestone. Learning Quickly When Irrelevant Attributes Abound: A New Linear-Threshold Algorithm , 1987, 28th Annual Symposium on Foundations of Computer Science (sfcs 1987).
[11] Terrence J. Sejnowski,et al. A 'Neural' Network that Learns to Play Backgammon , 1987, NIPS.
[12] Terrence J. Sejnowski,et al. Parallel Networks that Learn to Pronounce English Text , 1987, Complex Syst..
[13] Eric B. Baum,et al. On the capabilities of multilayer perceptrons , 1988, J. Complex..
[14] David Haussler,et al. Quantifying Inductive Bias: AI Learning Algorithms and Valiant's Learning Framework , 1988, Artif. Intell..
[15] Terrence J. Sejnowski,et al. NETtalk: a parallel network that learns to read aloud , 1988 .
[16] Leslie G. Valiant,et al. A general lower bound on the number of examples needed for learning , 1988, COLT '88.
[17] T. Sejnowski,et al. Predicting the secondary structure of globular proteins using neural network models. , 1988, Journal of molecular biology.
[18] Luc Devroye,et al. Automatic Pattern Recognition: A Study of the Probability of Error , 1988, IEEE Trans. Pattern Anal. Mach. Intell..
[19] Andrew M. Odlyzko,et al. On subspaces spanned by random selections of plus/minus 1 vectors , 1988, Journal of combinatorial theory. Series A.
[20] Esther Levin,et al. A statistical approach to learning and generalization in layered neural networks , 1989, Proc. IEEE.
[21] Y.S. Abu-Mostafa,et al. Information theory, complexity and neural networks , 1989, IEEE Communications Magazine.
[22] Jude Shavlik,et al. An Approach to Combining Explanation-based and Neural Learning Algorithms , 1989 .
[23] Halbert White,et al. Learning in Artificial Neural Networks: A Statistical Perspective , 1989, Neural Computation.
[24] Yaser S. Abu-Mostafa,et al. The Vapnik-Chervonenkis Dimension: Information versus Complexity in Learning , 1989, Neural Computation.
[25] V. Rich. Personal communication , 1989, Nature.
[26] Geoffrey E. Hinton. Connectionist Learning Procedures , 1989, Artif. Intell..
[27] Lawrence D. Jackel,et al. Backpropagation Applied to Handwritten Zip Code Recognition , 1989, Neural Computation.
[28] Eric B. Baum,et al. A Proposal for More Powerful Learning Algorithms , 1989, Neural Computation.
[29] David Haussler,et al. Learnability and the Vapnik-Chervonenkis dimension , 1989, JACM.
[30] Michael C. Mozer,et al. Using Relevance to Reduce Network Size Automatically , 1989 .
[31] Kishan G. Mehrotra,et al. Bounds on the number of samples needed for neural learning , 1991, IEEE Trans. Neural Networks.