Kenji Yamanishi Papers on DiSC'04 Distributed cooperative mining for information consortia Publications Note: Links lead to the DBLP on the Web. Kenji Yamanishi Satoshi Morinaga , Kenji Yamanishi, Jun-ichi Takeuchi : Distributed cooperative mining for information consortia. KDD 2003 : 619-624 Hang Li , Kenji Yamanishi: Topic analysis using a finite mixture model. Inf. Process. Manage. 39 (4): 521-541 (2003) Satoshi Morinaga , Kenji Yamanishi, Kenji Tateishi , Toshikazu Fukushima : Mining product reputations on the Web. KDD 2002 : 341-349 Kenji Yamanishi, Jun-ichi Takeuchi : A unifying framework for detecting outliers and change points from non-stationary time series data. KDD 2002 : 676-681 Kenji Yamanishi, Hang Li : Mining Open Answers in Questionnaire Data. IEEE Intelligent Systems 17 (5): 58-63 (2002) Hang Li , Kenji Yamanishi: Text classification using ESC-based stochastic decision lists. Inf. Process. Manage. 38 (3): 343-361 (2002) Kenji Yamanishi, Jun-ichi Takeuchi : Discovering outlier filtering rules from unlabeled data: combining a supervised learner with an unsupervised learner. KDD 2001 : 389-394 Hang Li , Kenji Yamanishi: Mining from open answers in questionnaire data. KDD 2001 : 443-449 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 Kenji Yamanishi: Extended Stochastic Complexity and Minimax Relative Loss Analysis. ATL 1999 : 26-38 Hang Li , Kenji Yamanishi: Text Classification Using ESC-based Stochastic Decision Lists. CIKM 1999 : 122-130 Kenji Yamanishi: Distributed Cooperative Bayesian Learning Strategies. Inf. Comput. 150 (1): 22-56 (1999) Kenji Yamanishi: Minimax Relative Loss Analysis for Sequential Prediction Algorithms Using Parametric Hypotheses. COLT 1998 : 32-43 Hang Li , Kenji Yamanishi: Document Classification Using a Finite Mixture Model. ACL 1997 : 39-47 Kenji Yamanishi: Distributed Cooperative Bayesian Learning Strategies. COLT 1997 : 250-262 Kenji Yamanishi: On-Line Maximum Likelihood Prediction with Respect to General Loss Functions. J. Comput. Syst. Sci. 55 (1): 105-118 (1997) Kenji Yamanishi: A Randomized Approximation of the MDL for Stochastic Models with Hidden Variables. COLT 1996 : 99-109 Kenji Yamanishi: Randomized Approximate Aggregating Strategies and Their Applications to Prediction and Discrimination. COLT 1995 : 83-90 Kenji Yamanishi: On-line maximum likelihood prediction with respect to general loss functions. EuroCOLT 1995 : 84-98 Hiroshi Mamitsuka , Kenji Yamanishi: alpha-Helix region prediction with stochastic rule learning. Computer Applications in the Biosciences 11 (4): 399-411 (1995) Kenji Yamanishi: A Loss Bound Model for On-Line Stochastic Prediction Algorithms Inf. Comput. 119 (1): 39-54 (1995) Kenji Yamanishi: Probably Almost Discriminative Learning. Machine Learning 18 (1): 23-50 (1995) Kenji Yamanishi: The Minimum L -Complexity Algorithm and its Applications to Learning Non-Parametric Rules. COLT 1994 : 173-182 Kenji Yamanishi: On Polynomial-Time Probably almost Discriminative Learnability. COLT 1993 : 94-100 Hiroshi Mamitsuka , Kenji Yamanishi: Protein Secondary Structure Prediction Based on Stochastic-Rule Learning. ALT 1992 : 240-251 Kenji Yamanishi: Probably Almost Discriminative Learning. COLT 1992 : 164-171 Kenji Yamanishi: A Learning Criterion for Stochastic Rules. Machine Learning 9 : 165-203 (1992) Kenji Yamanishi: A Loss Bound Model for On-Line Stochastic Prediction Strategies. COLT 1991 : 290-302 Kenji Yamanishi, Akihiko Konagaya : Learning Stochastic Motifs from Genetic Sequences. ML 1991 : 467-471 Kenji Yamanishi: A Learning Criterion for Stochastic Rules. COLT 1990 : 67-81 1 [ 28 ] 2 [ 2 ] 3 [ 17 ] [ 20 ] [ 23 ] [ 25 ] [ 26 ] [ 29 ] 4 [ 6 ] [ 11 ] 5 [ 22 ] 6 [ 28 ] [ 30 ] 7 [ 22 ] [ 24 ] [ 27 ] [ 30 ] 8 [ 28 ] 9 [ 22 ] ©2004 Association for Computing Machinery