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Note: Links lead to the DBLP on the Web. Kenji Yamanishi 24 Hang Li , Kenji Yamanishi: Text classification using ESC-based stochastic decision lists. Information Processing and Management 38 (3): 343-361 (2002) 23 Kenji Yamanishi, Jun-ichi Takeuchi : Discovering outlier filtering rules from unlabeled data: combining a supervised learner with an unsupervised learner. KDD 2001 : 389-394 22 Hang Li , Kenji Yamanishi: Mining from open answers in questionnaire data. KDD 2001 : 443-449 21 Kenji Yamanishi, Jun-ichi Takeuchi , Graham J. Williams , Peter Milne : On-line unsupervised outlier detection using finite mixtures with discounting learning algorithms. KDD 2000 : 320-324 20 Kenji Yamanishi: Extended Stochastic Complexity and Minimax Relative Loss Analysis. ATL 1999 : 26-38 19 Hang Li , Kenji Yamanishi: Text Classification Using ESC-based Stochastic Decision Lists. CIKM 1999 : 122-130 18 Kenji Yamanishi: Distributed Cooperative Bayesian Learning Strategies. Information and Computation 150 (1): 22-56 (1999) 17 Kenji Yamanishi: Minimax Relative Loss Analysis for Sequential Prediction Algorithms Using Parametric Hypotheses. COLT 1998 : 32-43 16 Hang Li , Kenji Yamanishi: Document Classification Using a Finite Mixture Model. ACL 1997 : 39-47 15 Kenji Yamanishi: Distributed Cooperative Bayesian Learning Strategies. COLT 1997 : 250-262 14 Kenji Yamanishi: On-Line Maximum Likelihood Prediction with Respect to General Loss Functions. JCSS 55 (1): 105-118 (1997) 13 Kenji Yamanishi: A Randomized Approximation of the MDL for Stochastic Models with Hidden Variables. COLT 1996 : 99-109 12 Kenji Yamanishi: Randomized Approximate Aggregating Strategies and Their Applications to Prediction and Discrimination. COLT 1995 : 83-90 11 Kenji Yamanishi: On-line maximum likelihood prediction with respect to general loss functions. EuroCOLT 1995 : 84-98 10 Kenji Yamanishi: A Loss Bound Model for On-Line Stochastic Prediction Algorithms. Information and Computation 119 (1): 39-54 (1995) 9 Kenji Yamanishi: Probably Almost Discriminative Learning. Machine Learning 18 (1): 23-50 (1995) 8 Kenji Yamanishi: The Minimum L -Complexity Algorithm and its Applications to Learning Non-Parametric Rules. COLT 1994 : 173-182 7 Kenji Yamanishi: On Polynomial-Time Probably almost Discriminative Learnability. COLT 1993 : 94-100 6 Hiroshi Mamitsuka , Kenji Yamanishi: Protein Secondary Structure Prediction Based on Stochastic-Rule Learning. ALT 1992 : 240-251 5 Kenji Yamanishi: Probably Almost Discriminative Learning. COLT 1992 : 164-171 4 Kenji Yamanishi: A Learning Criterion for Stochastic Rules. Machine Learning 9 : 165-203 (1992) 3 Kenji Yamanishi: A Loss Bound Model for On-Line Stochastic Prediction Strategies. COLT 1991 : 290-302 2 Kenji Yamanishi, Akihiko Konagaya : Learning Stochastic Motifs from Genetic Sequences. ML 1991 : 467-471 1 Kenji Yamanishi: A Learning Criterion for Stochastic Rules. COLT 1990 : 67-81  DiSC'02 © 2003 Association for Computing Machinery |
