Deterministic annealing EM algorithm

[1]  Masaomi Oda,et al.  Adaptability of K-L expansion technique for ambiguous face image retrieval , 1996 .

[2]  Geoffrey E. Hinton,et al.  The "wake-sleep" algorithm for unsupervised neural networks. , 1995, Science.

[3]  N. Ueda,et al.  Mixture density estimation via EM algorithm with deterministic annealing , 1994, Proceedings of IEEE Workshop on Neural Networks for Signal Processing.

[4]  Roy L. Streit,et al.  Maximum likelihood training of probabilistic neural networks , 1994, IEEE Trans. Neural Networks.

[5]  Alan L. Yuille,et al.  Statistical Physics, Mixtures of Distributions, and the EM Algorithm , 1994, Neural Computation.

[6]  Robert A. Jacobs,et al.  Hierarchical Mixtures of Experts and the EM Algorithm , 1993, Neural Computation.

[7]  Naonori Ueda,et al.  Deterministic Annealing Variant of the EM Algorithm , 1994, NIPS.

[8]  Michael I. Jordan,et al.  Supervised learning from incomplete data via an EM approach , 1993, NIPS.

[9]  Radford M. Neal A new view of the EM algorithm that justifies incremental and other variants , 1993 .

[10]  Joachim M. Buhmann,et al.  Complexity Optimized Data Clustering by Competitive Neural Networks , 1993, Neural Computation.

[11]  Yiu-Fai Wong,et al.  Clustering Data by Melting , 1993, Neural Computation.

[12]  M. Neal,et al.  A New View of the EM Algorithm thatJusti es Incremental and Other VariantsRadford , 1993 .

[13]  Geoffrey C. Fox,et al.  Vector quantization by deterministic annealing , 1992, IEEE Trans. Inf. Theory.

[14]  William J. Byrne,et al.  Alternating minimization and Boltzmann machine learning , 1992, IEEE Trans. Neural Networks.

[15]  Hans G. C. Tråvén,et al.  A neural network approach to statistical pattern classification by 'semiparametric' estimation of probability density functions , 1991, IEEE Trans. Neural Networks.

[16]  Biing-Hwang Juang,et al.  Hidden Markov Models for Speech Recognition , 1991 .

[17]  Donald F. Specht,et al.  Probabilistic neural networks , 1990, Neural Networks.

[18]  M. Feder Maximum entropy as a special case of the minimum description length criterion , 1986, IEEE Trans. Inf. Theory.

[19]  Donald Geman,et al.  Stochastic Relaxation, Gibbs Distributions, and the Bayesian Restoration of Images , 1984, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[20]  J. Rissanen,et al.  Modeling By Shortest Data Description* , 1978, Autom..

[21]  D. Rubin,et al.  Maximum likelihood from incomplete data via the EM - algorithm plus discussions on the paper , 1977 .