Bayesian methods for neural networks
暂无分享,去创建一个
[1] R. E. Kalman,et al. New Results in Linear Filtering and Prediction Theory , 1961 .
[2] E. Hannan. Testing for a Jump in the Spectral Function , 1961 .
[3] C. Striebel,et al. On the maximum likelihood estimates for linear dynamic systems , 1965 .
[4] Andrew H. Jazwinski,et al. Adaptive filtering , 1969, Autom..
[5] W. K. Hastings,et al. Monte Carlo Sampling Methods Using Markov Chains and Their Applications , 1970 .
[6] R. Mehra. On the identification of variances and adaptive Kalman filtering , 1970 .
[7] R. Mehra. On-line identification of linear dynamic systems with applications to Kalman filtering , 1971 .
[8] H. Sorenson,et al. Recursive bayesian estimation using gaussian sums , 1971 .
[9] G. Wahba,et al. A completely automatic french curve: fitting spline functions by cross validation , 1975 .
[10] R. Snee,et al. Ridge Regression in Practice , 1975 .
[11] B. Tapley,et al. Adaptive sequential estimation with unknown noise statistics , 1976 .
[12] G. Schwarz. Estimating the Dimension of a Model , 1978 .
[13] M. Stone. Cross-validation:a review 2 , 1978 .
[14] R. Engle,et al. Alternative Algorithms for the Estimation of Dynamic Factor , 1983 .
[15] Emile H. L. Aarts,et al. Simulated Annealing: Theory and Applications , 1987, Mathematics and Its Applications.
[16] G. Kitagawa. Non-Gaussian State—Space Modeling of Nonstationary Time Series , 1987 .
[17] Brian D. Ripley,et al. Stochastic Simulation , 2005 .
[18] Sharad Singhal,et al. Training Multilayer Perceptrons with the Extende Kalman Algorithm , 1988, NIPS.
[19] Yoh-Han Pao,et al. Adaptive pattern recognition and neural networks , 1989 .
[20] Yann LeCun,et al. Optimal Brain Damage , 1989, NIPS.
[21] Kurt Hornik,et al. Multilayer feedforward networks are universal approximators , 1989, Neural Networks.
[22] F. Girosi,et al. Networks for approximation and learning , 1990, Proc. IEEE.
[23] Kumpati S. Narendra,et al. Identification and control of dynamical systems using neural networks , 1990, IEEE Trans. Neural Networks.
[24] Mark E. Oxley,et al. Comparative Analysis of Backpropagation and the Extended Kalman Filter for Training Multilayer Perceptrons , 1992, IEEE Trans. Pattern Anal. Mach. Intell..
[25] L. Cooper,et al. When Networks Disagree: Ensemble Methods for Hybrid Neural Networks , 1992 .
[26] S Z Qin,et al. Comparison of four neural net learning methods for dynamic system identification , 1992, IEEE Trans. Neural Networks.
[27] Francesco Palmieri,et al. Optimal filtering algorithms for fast learning in feedforward neural networks , 1992, Neural Networks.
[28] L. Jones. A Simple Lemma on Greedy Approximation in Hilbert Space and Convergence Rates for Projection Pursuit Regression and Neural Network Training , 1992 .
[29] David J. C. MacKay,et al. Bayesian Interpolation , 1992, Neural Computation.
[30] Takayuki Yamada,et al. Dynamic system identification using neural networks , 1993, IEEE Trans. Syst. Man Cybern..
[31] George Cybenko,et al. Ill-Conditioning in Neural Network Training Problems , 1993, SIAM J. Sci. Comput..
[32] Jun S. Liu,et al. Sequential Imputations and Bayesian Missing Data Problems , 1994 .
[33] Nicholas G. Polson,et al. On the Geometric Convergence of the Gibbs Sampler , 1994 .
[34] Anthony J. Robinson,et al. An application of recurrent nets to phone probability estimation , 1994, IEEE Trans. Neural Networks.
[35] Peter M. Williams,et al. Bayesian Regularization and Pruning Using a Laplace Prior , 1995, Neural Computation.
[36] David Mackay,et al. Probable networks and plausible predictions - a review of practical Bayesian methods for supervised neural networks , 1995 .
[37] Todd K. Leen,et al. From Data Distributions to Regularization in Invariant Learning , 1995, Neural Computation.
[38] Yoshua Bengio,et al. Pattern Recognition and Neural Networks , 1995 .
[39] Visakan Kadirkamanathan,et al. Recursive Estimation of Dynamic Modular RBF Networks , 1995, NIPS.
[40] Robert A. Jacobs,et al. Methods For Combining Experts' Probability Assessments , 1995, Neural Computation.
[41] Jun S. Liu,et al. Blind Deconvolution via Sequential Imputations , 1995 .
[42] Lennart Ljung,et al. Nonlinear black-box modeling in system identification: a unified overview , 1995, Autom..
[43] Ah Chung Tsoi,et al. Local minima and generalization , 1996, Proceedings of International Conference on Neural Networks (ICNN'96).
[44] David Lowe,et al. Practical methods of tracking of nonstationary time series applied to real-world data , 1996, Defense + Commercial Sensing.
[45] Federico Girosi,et al. On the Relationship between Generalization Error, Hypothesis Complexity, and Sample Complexity for Radial Basis Functions , 1996, Neural Computation.
[46] R. Kohn,et al. Nonparametric regression using Bayesian variable selection , 1996 .
[47] Michael Isard,et al. Contour Tracking by Stochastic Propagation of Conditional Density , 1996, ECCV.
[48] T. Higuchi. Monte carlo filter using the genetic algorithm operators , 1997 .
[49] P. Green,et al. On Bayesian Analysis of Mixtures with an Unknown Number of Components (with discussion) , 1997 .
[50] Jun S. Liu,et al. Sequential Monte Carlo methods for dynamic systems , 1997 .
[51] Alan D. Marrs. An Application of Reversible-Jump MCMC to Multivariate Spherical Gaussian Mixtures , 1997, NIPS.
[52] W. A. Wright. Neural network regression with input uncertainty , 1998, Neural Networks for Signal Processing VIII. Proceedings of the 1998 IEEE Signal Processing Society Workshop (Cat. No.98TH8378).
[53] Peter Müller,et al. Issues in Bayesian Analysis of Neural Network Models , 1998, Neural Computation.
[54] Markus Hürzeler,et al. Monte Carlo Approximations for General State-Space Models , 1998 .
[55] Andrew Blake,et al. Learning dynamical models using expectation-maximisation , 1998, Sixth International Conference on Computer Vision (IEEE Cat. No.98CH36271).
[56] Gavin Smith,et al. Speech Modelling Using Subspace and EM Techniques , 1999, NIPS.
[57] Christophe Andrieu,et al. Robust Full Bayesian Methods for Neural Networks , 1999, NIPS.
[58] Jun S. Liu,et al. Sequential importance sampling for nonparametric Bayes models: The next generation , 1999 .
[59] Zoubin Ghahramani,et al. A Unifying Review of Linear Gaussian Models , 1999, Neural Computation.
[60] M. Pitt,et al. Filtering via Simulation: Auxiliary Particle Filters , 1999 .
[61] Hoon Kim,et al. Monte Carlo Statistical Methods , 2000, Technometrics.
[62] Andrew H. Gee,et al. Dynamic Learning with the EM Algorithm for Neural Networks , 2000, J. VLSI Signal Process..
[63] Andrew H. Gee,et al. Hierarchical Bayesian Models for Regularization in Sequential Learning , 2000, Neural Computation.
[64] Radford M. Neal. Annealed importance sampling , 1998, Stat. Comput..
[65] Nando de Freitas,et al. Sequential Monte Carlo Methods for Neural Networks , 2001, Sequential Monte Carlo Methods in Practice.