Kenji Yamanishi

	2009
41		Shunsuke Hirose, Kenji Yamanishi, Takayuki Nakata, Ryohei Fujimaki: Network anomaly detection based on Eigen equation compression. KDD 2009: 1185-1194
40		Ryohei Fujimaki, Takayuki Nakata, Hidenori Tsukahara, Akinori Sato, Kenji Yamanishi: Mining abnormal patterns from heterogeneous time-series with irrelevant features for fault event detection. Statistical Analysis and Data Mining 2(1): 1-17 (2009)
39		Shunsuke Hirose, Kenji Yamanishi: Latent variable mining with its applications to anomalous behavior detection. Statistical Analysis and Data Mining 2(1): 70-86 (2009)
	2008
38		Shunsuke Hirose, Kenji Yamanishi: Latent Variable Mining with Its Applications to Anomalous Behavior Detection. SDM 2008: 231-242
	2007
37		Kenji Yamanishi, Yuko Maruyama: Dynamic Model Selection With its Applications to Novelty Detection. IEEE Transactions on Information Theory 53(6): 2180-2189 (2007)
	2006
36		Jun-ichi Takeuchi, Kenji Yamanishi: A Unifying Framework for Detecting Outliers and Change Points from Time Series. IEEE Trans. Knowl. Data Eng. 18(4): 482-492 (2006)
	2005
35		Kenji Yamanishi, Yuko Maruyama: Dynamic syslog mining for network failure monitoring. KDD 2005: 499-508
	2004
34		Satoshi Morinaga, Kenji Yamanishi: Tracking dynamics of topic trends using a finite mixture model. KDD 2004: 811-816
33		Kenji Yamanishi, Jun-ichi Takeuchi, Graham J. Williams, Peter Milne: On-Line Unsupervised Outlier Detection Using Finite Mixtures with Discounting Learning Algorithms. Data Min. Knowl. Discov. 8(3): 275-300 (2004)
	2003
32		Satoshi Morinaga, Kenji Yamanishi, Jun-ichi Takeuchi: Distributed cooperative mining for information consortia. KDD 2003: 619-624
31		Hang Li, Kenji Yamanishi: Topic analysis using a finite mixture model. Inf. Process. Manage. 39(4): 521-541 (2003)
	2002
30		Satoshi Morinaga, Kenji Yamanishi, Kenji Tateishi, Toshikazu Fukushima: Mining product reputations on the Web. KDD 2002: 341-349
29		Kenji Yamanishi, Jun-ichi Takeuchi: A unifying framework for detecting outliers and change points from non-stationary time series data. KDD 2002: 676-681
28		Kenji Yamanishi, Hang Li: Mining Open Answers in Questionnaire Data. IEEE Intelligent Systems 17(5): 58-63 (2002)
27		Hang Li, Kenji Yamanishi: Text classification using ESC-based stochastic decision lists. Inf. Process. Manage. 38(3): 343-361 (2002)
	2001
26		Kenji Yamanishi, Jun-ichi Takeuchi: Discovering outlier filtering rules from unlabeled data: combining a supervised learner with an unsupervised learner. KDD 2001: 389-394
25		Hang Li, Kenji Yamanishi: Mining from open answers in questionnaire data. KDD 2001: 443-449
	2000
24		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
	1999
23		Kenji Yamanishi: Extended Stochastic Complexity and Minimax Relative Loss Analysis. ATL 1999: 26-38
22		Hang Li, Kenji Yamanishi: Text Classification Using ESC-based Stochastic Decision Lists. CIKM 1999: 122-130
21		Kenji Yamanishi: Distributed Cooperative Bayesian Learning Strategies. Inf. Comput. 150(1): 22-56 (1999)
	1998
20		Kenji Yamanishi: Minimax Relative Loss Analysis for Sequential Prediction Algorithms Using Parametric Hypotheses. COLT 1998: 32-43
19		Kenji Yamanishi: A Decision-Theoretic Extension of Stochastic Complexity and Its Applications to Learning. IEEE Transactions on Information Theory 44(4): 1424-1439 (1998)
	1997
18		Hang Li, Kenji Yamanishi: Document Classification Using a Finite Mixture Model. ACL 1997: 39-47
17		Kenji Yamanishi: Distributed Cooperative Bayesian Learning Strategies. COLT 1997: 250-262
16		Hang Li, Kenji Yamanishi: Document Classification Using a Finite Mixture Model CoRR cmp-lg/9705005: (1997)
15		Kenji Yamanishi: On-Line Maximum Likelihood Prediction with Respect to General Loss Functions. J. Comput. Syst. Sci. 55(1): 105-118 (1997)
	1996
14		Kenji Yamanishi: A Randomized Approximation of the MDL for Stochastic Models with Hidden Variables. COLT 1996: 99-109
	1995
13		Kenji Yamanishi: Randomized Approximate Aggregating Strategies and Their Applications to Prediction and Discrimination. COLT 1995: 83-90
12		Kenji Yamanishi: On-line maximum likelihood prediction with respect to general loss functions. EuroCOLT 1995: 84-98
11		Hiroshi Mamitsuka, Kenji Yamanishi: alpha-Helix region prediction with stochastic rule learning. Computer Applications in the Biosciences 11(4): 399-411 (1995)
10		Kenji Yamanishi: A Loss Bound Model for On-Line Stochastic Prediction Algorithms Inf. Comput. 119(1): 39-54 (1995)
9		Kenji Yamanishi: Probably Almost Discriminative Learning. Machine Learning 18(1): 23-50 (1995)
	1994
8		Kenji Yamanishi: The Minimum L-Complexity Algorithm and its Applications to Learning Non-Parametric Rules. COLT 1994: 173-182
	1993
7		Kenji Yamanishi: On Polynomial-Time Probably almost Discriminative Learnability. COLT 1993: 94-100
	1992
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)
	1991
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
	1990
1		Kenji Yamanishi: A Learning Criterion for Stochastic Rules. COLT 1990: 67-81

Coauthor Index

1	Ryohei Fujimaki	[40] [41]
2	Toshikazu Fukushima	[30]
3	Shunsuke Hirose	[38] [39] [41]
4	Akihiko Konagaya	[2]
5	Hang Li	[16] [18] [22] [25] [27] [28] [31]
6	Hiroshi Mamitsuka	[6] [11]
7	Yuko Maruyama	[35] [37]
8	Peter Milne	[24] [33]
9	Satoshi Morinaga	[30] [32] [34]
10	Takayuki Nakata	[40] [41]
11	Akinori Sato	[40]
12	Jun-ichi Takeuchi	[24] [26] [29] [32] [33] [36]
13	Kenji Tateishi	[30]
14	Hidenori Tsukahara	[40]
15	Graham J. Williams	[24] [33]

Colors in the list of coauthors