| |
 |      |
                                         |
Note: Links lead to the DBLP on the Web. Andrew Y. Ng 22 Susan T. Dumais , Michele Banko , Eric Brill , Jimmy J. Lin , Andrew Y. Ng: Web question answering: is more always better?. SIGIR 2002 : 291-298 21 Michael J. Kearns , Yishay Mansour , Andrew Y. Ng: A Sparse Sampling Algorithm for Near-Optimal Planning in Large Markov Decision Processes. Machine Learning 49 (2-3): 193-208 (2002) 20 Andrew Y. Ng, Michael I. Jordan : Convergence rates of the Voting Gibbs classifier, with application to Bayesian feature selection. ICML 2001 : 377-384 19 Andrew Y. Ng, Alice X. Zheng , Michael I. Jordan : Link Analysis, Eigenvectors and Stability. IJCAI 2001 : 903-910 18 Andrew Y. Ng, Michael I. Jordan : On Discriminative vs. Generative Classifiers: A comparison of logistic regression and naive Bayes. NIPS 2001 : 841-848 17 Andrew Y. Ng, Michael I. Jordan , Y. Weiss : On Spectral Clustering: Analysis and an algorithm. NIPS 2001 : 849-856 16 Alice X. Zheng , Andrew Y. Ng, Michael I. Jordan : Stable Algorithms for Link Analysis. SIGIR 2001 : 258-266 15 Eric Brill , Jimmy J. Lin , Michele Banko , Susan T. Dumais , Andrew Y. Ng: Data-Intensive Question Answering. TREC 2001 14 Andrew Y. Ng, Stuart J. Russell : Algorithms for Inverse Reinforcement Learning. ICML 2000 : 663-670 13 Andrew Y. Ng, Michael I. Jordan : PEGASUS: A policy search method for large MDPs and POMDPs. UAI 2000 : 406-415 12 Andrew Y. Ng, Daishi Harada , Stuart J. Russell : Policy Invariance Under Reward Transformations: Theory and Application to Reward Shaping. ICML 1999 : 278-287 11 Michael J. Kearns , Yishay Mansour , Andrew Y. Ng: A Sparse Sampling Algorithm for Near-Optimal Planning in Large Markov Decision Processes. IJCAI 1999 : 1324-1231 10 Michael J. Kearns , Yishay Mansour , Andrew Y. Ng: Approximate Planning in Large POMDPs via Reusable Trajectories. NIPS 1999 : 1001-1007 9 Andrew Y. Ng, Ronald Parr , Daphne Koller : Policy Search via Density Estimation. NIPS 1999 : 1022-1028 8 Andrew Y. Ng, Michael I. Jordan : Approximate Inference A lgorithms for Two-Layer Bayesian Networks. NIPS 1999 : 533-539 7 Scott Davies , Andrew Y. Ng, Andrew Moore : Applying Online Search Techniques to Continuous-State Reinforcement Learning. AAAI/IAAI 1998 : 753-760 6 Andrew McCallum , Ronald Rosenfeld , Tom M. Mitchell , Andrew Y. Ng: Improving Text Classification by Shrinkage in a Hierarchy of Classes. ICML 1998 : 359-367 5 Andrew Y. Ng: On Feature Selection: Learning with Exponentially Many Irrelevant Features as Training Examples. ICML 1998 : 404-412 4 Andrew Y. Ng: Preventing "Overfitting" of Cross-Validation Data. ICML 1997 : 245-253 3 Michael J. Kearns , Yishay Mansour , Andrew Y. Ng: An Information-Theoretic Analysis of Hard and Soft Assignment Methods for Clustering. UAI 1997 : 282-293 2 Michael J. Kearns , Yishay Mansour , Andrew Y. Ng, Dana Ron : An Experimental and Theoretical Comparison of Model Selection Methods. Machine Learning 27 (1): 7-50 (1997) 1 Michael J. Kearns , Yishay Mansour , Andrew Y. Ng, Dana Ron : An Experimental and Theoretical Comparison of Model Selection Methods. COLT 1995 : 21-30  DiSC'03 © 2003 Association for Computing Machinery |
