Bayesian Methods for Adaptive Models
暂无分享,去创建一个
Peter Cheeseman | Radford M. Neal | Haim Sompolinsky | David Robinson | Ken Rose | Sidney Fels | Sibusiso Sibisi | John Bridle | John Skilling | Andreas V. M. Herz | David Koerner | John J. Hopfield | Ken Miller | Steve Gull | Doug Kerns | Allen Knutsen | Mike Lewicki | Tom Loredo | Steve Luttrell | Ronny Meir | Marcus Mitchell | Steve Nowlan | J. Hopfield | S. Nowlan | J. Bridle | R. Meir | H. Sompolinsky | J. Skilling | S. Gull | M. Lewicki | T. Loredo | P. Cheeseman | S. Luttrell | S. Fels | A. Herz | Doug Kerns | Allen Knutsen | David Koerner | Ken Miller | Marcus Mitchell | D. Robinson | K. Rose | S. Sibisi
[1] G. G. Stokes. "J." , 1890, The New Yale Book of Quotations.
[2] L. M. M.-T.. Theory of Probability , 1929, Nature.
[3] D. Lindley. On a Measure of the Information Provided by an Experiment , 1956 .
[4] G. C. Tiao,et al. A Further Look at Robustness via Bayes's Theorem , 1962 .
[5] G. C. Tiao,et al. A Bayesian approach to the importance of assumptions applied to the comparison of variances , 1964 .
[6] G. C. Tiao,et al. A bayesian approach to some outlier problems. , 1968, Biometrika.
[7] C. S. Wallace,et al. An Information Measure for Classification , 1968, Comput. J..
[8] A. M. Walker. On the Asymptotic Behaviour of Posterior Distributions , 1969 .
[9] H. Akaike. Statistical predictor identification , 1970 .
[10] G. C. Tiao,et al. Bayesian inference in statistical analysis , 1973 .
[11] Richard O. Duda,et al. Pattern classification and scene analysis , 1974, A Wiley-Interscience publication.
[12] I. Good,et al. Information, weight of evidence, the singularity between probability measures and signal detection , 1974 .
[13] M. Goldstein. Bayesian analysis of regression problems , 1976 .
[14] R. Kashyap. A Bayesian comparison of different classes of dynamic models using empirical data , 1977 .
[15] G. Schwarz. Estimating the Dimension of a Model , 1978 .
[16] J. Rissanen,et al. Modeling By Shortest Data Description* , 1978, Autom..
[17] D. Spiegelhalter,et al. Bayes Factors and Choice Criteria for Linear Models , 1980 .
[18] C. S. Wallace,et al. Archaeoastronomy in the Old World: STONE CIRCLE GEOMETRIES: AN INFORMATION THEORY APPROACH , 1982 .
[19] A. Zellner,et al. Basic Issues in Econometrics. , 1986 .
[20] D. Titterington. Common structure of smoothing techniques in statistics , 1985 .
[21] Tomaso Poggio,et al. Computational vision and regularization theory , 1985, Nature.
[22] S. Luttrell. The use of transinformation in the design of data sampling schemes for inverse problems , 1985 .
[23] M. F.,et al. Bibliography , 1985, Experimental Gerontology.
[24] Geoffrey E. Hinton,et al. Learning representations by back-propagating errors , 1986, Nature.
[25] A. R. Davies,et al. Optimisation in the regularisation ill-posed problems , 1986, The Journal of the Australian Mathematical Society. Series B. Applied Mathematics.
[26] Geoffrey E. Hinton,et al. Learning representations by back-propagation errors, nature , 1986 .
[27] J. Justice. Maximum entropy and bayesian methods in applied statistics , 1986 .
[28] Geoffrey E. Hinton,et al. Learning and relearning in Boltzmann machines , 1986 .
[29] S. Stigler. Laplace's 1774 Memoir on Inverse Probability , 1986 .
[30] C. S. Wallace,et al. Estimation and Inference by Compact Coding , 1987 .
[31] David Lindley,et al. Bayesian Statistics, a Review , 1987 .
[32] J J Hopfield,et al. Learning algorithms and probability distributions in feed-forward and feed-back networks. , 1987, Proceedings of the National Academy of Sciences of the United States of America.
[33] Esther Levin,et al. Accelerated Learning in Layered Neural Networks , 1988, Complex Syst..
[34] S. Gull. Bayesian Inductive Inference and Maximum Entropy , 1988 .
[35] J. Berger. Statistical Decision Theory and Bayesian Analysis , 1988 .
[36] Esther Levin,et al. A statistical approach to learning and generalization in layered neural networks , 1989, Proc. IEEE.
[37] John Scott Bridle,et al. Probabilistic Interpretation of Feedforward Classification Network Outputs, with Relationships to Statistical Pattern Recognition , 1989, NATO Neurocomputing.
[38] J. Skilling. The Eigenvalues of Mega-dimensional Matrices , 1989 .
[39] Yann LeCun,et al. Improving the convergence of back-propagation learning with second-order methods , 1989 .
[40] M. C. Jones,et al. Spline Smoothing and Nonparametric Regression. , 1989 .
[41] Yaser S. Abu-Mostafa,et al. The Vapnik-Chervonenkis Dimension: Information versus Complexity in Learning , 1989, Neural Computation.
[42] Stephen F. Gull,et al. Developments in Maximum Entropy Data Analysis , 1989 .
[43] David S. Touretzky,et al. Advances in neural information processing systems 2 , 1989 .
[44] Fernando J. Pineda,et al. Recurrent Backpropagation and the Dynamical Approach to Adaptive Neural Computation , 1989, Neural Computation.
[45] Naftali Tishby,et al. Consistent inference of probabilities in layered networks: predictions and generalizations , 1989, International 1989 Joint Conference on Neural Networks.
[46] Stephen F. Gull,et al. Bayesian Data Analysis: Straight-line fitting , 1989 .
[47] T. Loredo. From Laplace to Supernova SN 1987A: Bayesian Inference in Astrophysics , 1990 .
[48] R. T. Cox. Probability, frequency and reasonable expectation , 1990 .
[49] J. Angel,et al. Adaptive optics for array telescopes using neural-network techniques , 1990, Nature.
[50] G. L. Bretthorst. Bayesian analysis. I. Parameter estimation using quadrature NMR models , 1990 .
[51] David J. Spiegelhalter,et al. Sequential updating of conditional probabilities on directed graphical structures , 1990, Networks.
[52] Yaser S. Abu-Mostafa,et al. Learning from hints in neural networks , 1990, J. Complex..
[53] Yann LeCun,et al. Transforming Neural-Net Output Levels to Probability Distributions , 1990, NIPS.
[54] Chuanyi Ji,et al. Generalizing Smoothness Constraints from Discrete Samples , 1990, Neural Computation.
[55] Isabelle Guyon,et al. Structural Risk Minimization for Character Recognition , 1991, NIPS.
[56] Peter Cheeseman,et al. Bayesian classification theory , 1991 .
[57] N. Weir,et al. Applications of Maximum Entropy Techniques to HST Data , 1991 .
[58] Steven J. Nowlan,et al. Soft competitive adaptation: neural network learning algorithms based on fitting statistical mixtures , 1991 .
[59] 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.
[60] Mahmoud A. El-Gamal. The Role of Priors in Active Bayesian Learning in the Sequential Statistical Decision Framework , 1991 .
[61] M. Charter. Quantifying Drug Absorption , 1991 .
[62] Eric B. Baum,et al. Neural net algorithms that learn in polynomial time from examples and queries , 1991, IEEE Trans. Neural Networks.
[63] D. Mackay,et al. A Practical Bayesian Framework for Backprop Networks , 1991 .
[64] Wei Tsih Lee,et al. On Optimal Adaptive Classifier Design Criterion- How many hidden units are necessary for an optimal neural network classifier? , 1991 .
[65] T. Bayes. An essay towards solving a problem in the doctrine of chances , 2003 .
[66] J. Skilling. On Parameter Estimation and Quantified Maxent , 1991 .
[67] Jenq-Neng Hwang,et al. Query-based learning applied to partially trained multilayer perceptrons , 1991, IEEE Trans. Neural Networks.
[68] David J. C. MacKay,et al. Information-Based Objective Functions for Active Data Selection , 1992, Neural Computation.
[69] Sompolinsky,et al. Statistical mechanics of learning from examples. , 1992, Physical review. A, Atomic, molecular, and optical physics.
[70] Chris Bishop,et al. Current address: Microsoft Research, , 2022 .
[71] J. Skilling. Bayesian Solution of Ordinary Differential Equations , 1992 .
[72] James O. Berger,et al. Ockham's Razor and Bayesian Analysis , 1992 .
[73] David J. C. MacKay,et al. The Evidence Framework Applied to Classification Networks , 1992, Neural Computation.
[74] K. Mark,et al. Bayesian model selection and minimum description length estimation of auditory-nerve discharge rates. , 1992, The Journal of the Acoustical Society of America.
[75] Radford M. Neal. Bayesian training of backpropagation networks by the hybrid Monte-Carlo method , 1992 .
[76] Hua Lee,et al. Maximum Entropy and Bayesian Methods. , 1996 .
[77] William H. Press,et al. Numerical recipes in C , 2002 .
[78] Richard Szeliski,et al. Bayesian modeling of uncertainty in low-level vision , 2011, International Journal of Computer Vision.