Convergence results for the EM approach to mixtures of experts architectures

[1]  Michael I. Jordan,et al.  On Convergence Properties of the EM Algorithm for Gaussian Mixtures , 1996, Neural Computation.

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

[3]  Michael I. Jordan,et al.  Hierarchies of Adaptive Experts , 1991, NIPS.

[4]  J. Friedman Multivariate adaptive regression splines , 1990 .

[5]  Geoffrey E. Hinton,et al.  Adaptive Mixtures of Local Experts , 1991, Neural Computation.

[6]  I. Meilijson A fast improvement to the EM algorithm on its own terms , 1989 .

[7]  D. N. Geary Mixture Models: Inference and Applications to Clustering , 1989 .

[8]  D. Rubin,et al.  Statistical Analysis with Missing Data. , 1989 .

[9]  R. D. Veaux Parameter estimation for a mixture of linear regressions (em algorithm, asymptotic efficiency) , 1986 .

[10]  R. Redner,et al.  Mixture densities, maximum likelihood, and the EM algorithm , 1984 .

[11]  New York Dover,et al.  ON THE CONVERGENCE PROPERTIES OF THE EM ALGORITHM , 1983 .

[12]  J. B. Ramsey,et al.  Estimating Mixtures of Normal Distributions and Switching Regressions , 1978 .

[13]  H. Walker,et al.  THE NUMERICAL EVALUATION OF THE MAXIMUM-LIKELIHOOD ESTIMATE OF A SUBSET OF MIXTURE PROPORTIONS* , 1978 .

[14]  H. Walker,et al.  An iterative procedure for obtaining maximum-likelihood estimates of the parameters for a mixture of normal distributions , 1978 .

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

[16]  R. Quandt A New Approach to Estimating Switching Regressions , 1972 .

[17]  L. Baum,et al.  A Maximization Technique Occurring in the Statistical Analysis of Probabilistic Functions of Markov Chains , 1970 .

[18]  L. Baum,et al.  Growth transformations for functions on manifolds. , 1968 .

[19]  W. Loh,et al.  Classification and regression trees , 2022 .