C
  • Published in: Neural Computation, 6, 181214, 1994.
    Hierarchical mixtures of experts and the EM algorithm
    Michael I. Jordan
    Department of Brain and Cognitive Sciences Massachusetts Institute of Technology
    Robert A. Jacobs
    Department of Psychology University of Rochester
  • CS 281 Machine Learning
    Spring 1998 Stuart Russell The EM Algorithm
  • Communicated by Chris Williams
    Asymptotic Convergence Rate of the EM Algorithm for Gaussian Mixtures
    Jinwen Ma
    Department of Computer Science & Engineering, The Chinese University of Hong Kong, Shatin Hong Kong and Institute of Mathematics, Shantou University, Shantou, Guangdong, 515063, Peoples Republic of China
    Lei Xu Department of Computer Science & Engineering, The Chinese University of Hong Kong, Shatin Hong Kong, Peoples Republic of China
    Michael I. Jordan
    Department of Computer Science and Department of Statistics, University of California at Berkeley, Berkeley, CA 94720, U.S.A.
  • DiscriminantEM Algorithm with Application to Image Retrieval
    Ying Wu, Qi Tian, Thomas S. Huang
    Beckman Institute University of Illinois at UrbanaChampaign Urbana, IL 61801
  • Information Geometry of the EM and em Algorithms for Neural Networks
    Shunichi Amari
    Department of Mathematical Engineering and Information Physics Faculty of Engineering, University of Tokyo Bunkyoku, Tokyo 113, JAPAN
  • Neural Computation, 8, 129--151, 1996.
    On Convergence Properties of the EM Algorithm for Gaussian Mixtures
    Lei Xu
    Department of Computer Science The Chinese University of Hong Kong
    Michael I. Jordan Department of Brain and Cognitive Sciences Massachusetts Institute of Technology
  • SUFFICIENT CONDITIONS FOR NORM CONVERGENCE OF THE EM ALGORITHM
    Alfred O. Hero
    Department of EECS University of Michigan Ann Arbor, MI 48109

    Jeffrey A. Fessler
    Division of Nuclear Medicine University of Michigan Ann Arbor, MI 48109
  • Training Algorithms for Hidden Markov Models Using Entropy Based Distance Functions
    Yoram Singer
    AT&T Laboratories 600 Mountain Avenue Murray Hill, NJ 07974
    Manfred K. Warmuth
    Computer Science Department University of California Santa Cruz, CA 95064
  • INTERNATIONAL COMPUTER SCIENCE INSTITUTE
    1947 Center Street ffl Suite 600 ffl Berkeley, California 94704 ffl 15106424274 ffl FAX 15106437684

    Mixture Models and the EM Algorithm for Object Recognition within Compositional Hierarchies Part 1: Recognition
    Joachim Utans January 1993
  • Learning Simple Relations: Theory and Applications
    Pavel Berkhin and Jonathan D. Becher
    Accrue Software, Inc., 48634 Milmont Drive, Fremont, CA 94538
  • A Comparison of Document Clustering Techniques
    Michael Steinbach, George Karypis, Vipin Kumar
    Department of Computer Science and Egineering, University of Minnesota Technical Report #00-034
  • HMM (1)
  • Visual Speech And Speaker Recognition
    Juergen Luettin
    Department of Computer Science University of Sheffield Dissertation submitted to the University of Sheffield for the degree of Doctor of Philosophy
    May 1997
  • CIRCULAR VITERBI BASED ADAPTIVE SYSTEM FOR AUTOMATIC VIDEO OBJECT SEGMENTATION
    IJong Lin, S.Y. Kung
  • HMM (2)
  • TRAINING MANY SMALL HIDDEN MARKOV MODELS
    G. E. Hinton
    Gatsby Computational Neuroscience Unit, University College London, 17 Queen Square, London WC1N 3AR.
    A. D. Brown
    Department of Computer Science, University of Toronto, Toronto, Ontario M5S 3G4 Canada.
  • Li & al.,IEEE Transactions on PAMI, vol. PAMI-22, no. 4, pp 371-377, April 2000. 1
    Training Hidden Markov Models with Multiple Observations A Combinatorial Method
    Xiaolin Li, Member, IEEE Computer Society CADlink Technology Corporation, 2440 Don Reid Drive, Suite 100, Ottawa, Ontario, Canada K1H 1E1.
    Marc Parizeau, Member, IEEE Computer Society Departement de Genie Electrique et de Genie Informatique, Universite Laval, Ste-Foy, Quebec, Canada G1K 7P4.
    Rejean Plamondon, Fellow, IEEE Ecole Polytechnique de Montreal, Montreal, Quebec, Canada H3C 3A7.
  • IEEE TRANSACTIONS ON SPEECH AND AUDIO PROCESSING, VOL. 9, NO. 4, MAY 2001 411
    A Modified BaumWelch Algorithm for Hidden Markov Models with Multiple Observation Spaces
    Paul M. Baggenstoss, Member, IEEE
  • Improved Ensemble Training for Hidden Markov Models using Random Relative Node Permutations
    Richard I. A. Davis and Brian C. Lovell Intelligent Real-Time Imaging and Sensing Group, School of Information Technology and Electrical Engineering, The University of Queensland, Australia, 4072