Probabilistic Learning Models

The purpose of this review is to provide a brief outline of some uses of Bayesian methods in artificial intelligence, specifically in the area of neural computation.

[1]  David J. C. MacKay,et al.  A Practical Bayesian Framework for Backpropagation Networks , 1992, Neural Computation.

[2]  A. Barron Approximation and Estimation Bounds for Artificial Neural Networks , 1991, COLT '91.

[3]  P. Kitanidis Parameter Uncertainty in Estimation of Spatial Functions: Bayesian Analysis , 1986 .

[4]  Olivier Chapelle,et al.  Model Selection for Support Vector Machines , 1999, NIPS.

[5]  Michael E. Tipping The Relevance Vector Machine , 1999, NIPS.

[6]  Sayan Mukherjee,et al.  Support Vector Method for Multivariate Density Estimation , 1999, NIPS.

[7]  Alexander J. Smola,et al.  Support Vector Method for Function Approximation, Regression Estimation and Signal Processing , 1996, NIPS.

[8]  David Barber,et al.  Ensemble Learning for Multi-Layer Networks , 1997, NIPS.

[9]  Christopher M. Bishop,et al.  Current address: Microsoft Research, , 2022 .

[10]  James L. McClelland,et al.  Parallel distributed processing: explorations in the microstructure of cognition, vol. 1: foundations , 1986 .

[11]  Nello Cristianini,et al.  An introduction to Support Vector Machines , 2000 .

[12]  Mike Rees,et al.  5. Statistics for Spatial Data , 1993 .

[13]  Peter M. Williams,et al.  Matrix logarithm parametrizations for neural network covariance models , 1999, Neural Networks.

[14]  Matthias W. Seeger,et al.  Using the Nyström Method to Speed Up Kernel Machines , 2000, NIPS.

[15]  Peter M. Williams,et al.  Modelling Seasonality and Trends in Daily Rainfall Data , 1997, NIPS.

[16]  P. M. Williams,et al.  Using Neural Networks to Model Conditional Multivariate Densities , 1996, Neural Computation.

[17]  Wray L. Buntine,et al.  Bayesian Back-Propagation , 1991, Complex Syst..

[18]  Peter M. Williams,et al.  Bayesian Regularization and Pruning Using a Laplace Prior , 1995, Neural Computation.

[19]  J. W. Miskin,et al.  Ensemble Learning for Blind Source Separation , 2001 .

[20]  Christopher M. Bishop,et al.  Estimating Conditional Probability Densities for Periodic Variables , 1994, NIPS.

[21]  Matthias W. Seeger,et al.  Bayesian Model Selection for Support Vector Machines, Gaussian Processes and Other Kernel Classifiers , 1999, NIPS.

[22]  John Shawe-Taylor,et al.  Generalization Performance of Support Vector Machines and Other Pattern Classifiers , 1999 .

[24]  H. Jeffreys,et al.  Theory of probability , 1896 .

[25]  Vladimir Vapnik,et al.  Statistical learning theory , 1998 .

[26]  Teuvo Kohonen,et al.  Self-Organizing Maps , 2010 .

[27]  Pierre Comon,et al.  Independent component analysis, A new concept? , 1994, Signal Process..

[28]  A. N. Tikhonov,et al.  Solutions of ill-posed problems , 1977 .

[29]  Geoffrey E. Hinton,et al.  Keeping the neural networks simple by minimizing the description length of the weights , 1993, COLT '93.

[30]  Carl E. Rasmussen,et al.  In Advances in Neural Information Processing Systems , 2011 .

[31]  Manfred Opper,et al.  Finite-Dimensional Approximation of Gaussian Processes , 1998, NIPS.

[32]  Brian D. Ripley,et al.  Pattern Recognition and Neural Networks , 1996 .

[33]  Radford M. Neal Priors for Infinite Networks , 1996 .

[34]  Andrzej Cichocki,et al.  A New Learning Algorithm for Blind Signal Separation , 1995, NIPS.

[35]  Zoubin Ghahramani,et al.  A Unifying Review of Linear Gaussian Models , 1999, Neural Computation.

[36]  Kurt Hornik,et al.  Some new results on neural network approximation , 1993, Neural Networks.

[37]  V. Vapnik,et al.  Bounds on Error Expectation for Support Vector Machines , 2000, Neural Computation.

[38]  Terrence J. Sejnowski,et al.  An Information-Maximization Approach to Blind Separation and Blind Deconvolution , 1995, Neural Computation.

[39]  Christopher M. Bishop,et al.  Developments of the generative topographic mapping , 1998, Neurocomputing.

[40]  David J. C. MacKay,et al.  Developments in Probabilistic Modelling with Neural Networks - Ensemble Learning , 1995, SNN Symposium on Neural Networks.

[41]  David J. C. MacKay,et al.  Bayesian Interpolation , 1992, Neural Computation.

[42]  John Shawe-Taylor,et al.  Structural Risk Minimization Over Data-Dependent Hierarchies , 1998, IEEE Trans. Inf. Theory.

[43]  Geoffrey E. Hinton,et al.  Learning distributed representations of concepts. , 1989 .

[44]  Malik Magdon-Ismail,et al.  No Free Lunch for Early Stopping , 1999, Neural Computation.

[45]  Christopher K. I. Williams Computation with Infinite Neural Networks , 1998, Neural Computation.

[46]  Christopher M. Bishop,et al.  Neural networks for pattern recognition , 1995 .

[47]  J. Zidek,et al.  Interpolation with uncertain spatial covariances: a Bayesian alternative to Kriging , 1992 .

[48]  M. Gibbs,et al.  Efficient implementation of gaussian processes , 1997 .

[49]  Geoffrey E. Hinton,et al.  Bayesian Learning for Neural Networks , 1995 .

[50]  Christoph E. Schreiner,et al.  Blind source separation and deconvolution: the dynamic component analysis algorithm , 1998 .

[51]  Christopher M. Bishop,et al.  GTM: The Generative Topographic Mapping , 1998, Neural Computation.