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Note: Links lead to the DBLP on the Web. Michael I. Jordan 25 Andrew Y. Ng , Alice X. Zheng , Michael I. Jordan: Link Analysis, Eigenvectors and Stability. IJCAI 2001 : 903-910 24 Alice X. Zheng , Andrew Y. Ng , Michael I. Jordan: Stable Algorithms for Link Analysis. SIGIR 2001 : 258-266 23 Jinwen Ma , Lei Xu , Michael I. Jordan: Asymptotic Convergence Rate of the EM Algorithm for Gaussian Mixtures. Neural Computation 12 (12): 2881-2907 (2001) 22 Marina Meila , Michael I. Jordan: Learning with Mixtures of Trees. Journal of Machine Learning Research 1 : 1-48 (2000) 21 Lawrence K. Saul , Michael I. Jordan: Attractor Dynamics in Feedforward Neural Networks. Neural Computation 12 (6): 1313-1335 (2000) 20 Tommi Jaakkola , Michael I. Jordan: Variational Probabilistic Inference and the QMR-DT Network. JAIR 10 : 291-322 (1999) 19 Lawrence K. Saul , Michael I. Jordan: Mixed Memory Markov Models: Decomposing Complex Stochastic Processes as Mixtures of Simpler Ones. Machine Learning 37 (1): 75-87 (1999) 18 Michael I. Jordan, Zoubin Ghahramani , Tommi Jaakkola , Lawrence K. Saul : An Introduction to Variational Methods for Graphical Models. Machine Learning 37 (2): 183-233 (1999) 17 Michael I. Jordan, Christopher M. Bishop : Neural Networks. The Computer Science and Engineering Handbook 1997 : 536-556 16 Zoubin Ghahramani , Michael I. Jordan: Factorial Hidden Markov Models. Machine Learning 29 (2-3): 245-273 (1997) 15 Padhraic Smyth , David Heckerman , Michael I. Jordan: Probabilistic Independence Networks for Hidden Markov Probability Models. Neural Computation 9 (2): 227-269 (1997) 14 Michael I. Jordan, Christopher M. Bishop : Neural Networks. ACM Computing Surveys 28 (1): 73-75 (1996) 13 David A. Cohn , Zoubin Ghahramani , Michael I. Jordan: Active Learning with Statistical Models. JAIR 4 : 129-145 (1996) 12 Lawrence K. Saul , Tommi Jaakkola , Michael I. Jordan: Mean Field Theory for Sigmoid Belief Networks. JAIR 4 : 61-76 (1996) 11 Michael I. Jordan: A Statistical Approach to Decision Tree Modeling. COLT 1994 : 13-20 10 Satinder P. Singh , Tommi Jaakkola , Michael I. Jordan: Learning Without State-Estimation in Partially Observable Markovian Decision Processes. ICML 1994 : 284-292 9 Michael I. Jordan: A Statistical Approach to Decision Tree Modeling. ICML 1994 : 363-370 8 Michael I. Jordan, Robert A. Jacobs : Supervised Learning and Divide-and-Conquer: A Statistical Approach. ICML 1993 : 159-166 7 Robert A. Jacobs , Michael I. Jordan, Andrew G. Barto : Task Decompostiion Through Competition in a Modular Connectionist Architecture: The What and Where Vision Tasks. Machine Learning: From Theory to Applications 1993 : 175-202 6 Daphne Bavelier , Michael I. Jordan: A Dynamical Model of Priming and Repetition Blindness. NIPS 1992 : 879-886 5 Michael I. Jordan, David E. Rumelhart : Forward Models: Supervised Learning with a Distal Teacher. Cognitive Science 16 (3): 307-354 (1992) 4 Michael I. Jordan, David E. Rumelhart : Internal World Models and Supervised Learning. ML 1991 : 70-74 3 Robert A. Jacobs , Michael I. Jordan, Andrew G. Barto : Task Decomposition Through Competition in a Modular Connectionist Architecture: The What and Where Vision Tasks. Cognitive Science 15 (2): 219-250 (1991) 2 Robert A. Jacobs , Michael I. Jordan: A Competitive Modular Connectionist Architecture. NIPS 1990 : 767-773 1 Michael I. Jordan, Robert A. Jacobs : Learning to Control an Unstable System with Forward Modeling. NIPS 1989 : 324-331  DiSC'02 © 2003 Association for Computing Machinery |
