| 2008 |
|---|
| 38 | EE | Shunsuke Hirose,
Kenji Yamanishi:
Latent Variable Mining with Its Applications to Anomalous Behavior Detection.
SDM 2008: 231-242 |
| 2007 |
|---|
| 37 | EE | 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 | EE | 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 | EE | Kenji Yamanishi,
Yuko Maruyama:
Dynamic syslog mining for network failure monitoring.
KDD 2005: 499-508 |
| 2004 |
|---|
| 34 | EE | Satoshi Morinaga,
Kenji Yamanishi:
Tracking dynamics of topic trends using a finite mixture model.
KDD 2004: 811-816 |
| 33 | EE | 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 | EE | Satoshi Morinaga,
Kenji Yamanishi,
Jun-ichi Takeuchi:
Distributed cooperative mining for information consortia.
KDD 2003: 619-624 |
| 31 | EE | Hang Li,
Kenji Yamanishi:
Topic analysis using a finite mixture model.
Inf. Process. Manage. 39(4): 521-541 (2003) |
| 2002 |
|---|
| 30 | EE | Satoshi Morinaga,
Kenji Yamanishi,
Kenji Tateishi,
Toshikazu Fukushima:
Mining product reputations on the Web.
KDD 2002: 341-349 |
| 29 | EE | 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 | EE | 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 | EE | 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 | EE | Hang Li,
Kenji Yamanishi:
Mining from open answers in questionnaire data.
KDD 2001: 443-449 |
| 2000 |
|---|
| 24 | EE | 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 | EE | Kenji Yamanishi:
Extended Stochastic Complexity and Minimax Relative Loss Analysis.
ATL 1999: 26-38 |
| 22 | EE | 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 | EE | 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 | EE | Kenji Yamanishi:
Distributed Cooperative Bayesian Learning Strategies.
COLT 1997: 250-262 |
| 16 | EE | 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 | EE | Kenji Yamanishi:
A Randomized Approximation of the MDL for Stochastic Models with Hidden Variables.
COLT 1996: 99-109 |
| 1995 |
|---|
| 13 | EE | 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 | EE | Kenji Yamanishi:
The Minimum L-Complexity Algorithm and its Applications to Learning Non-Parametric Rules.
COLT 1994: 173-182 |
| 1993 |
|---|
| 7 | EE | 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 | EE | 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 | EE | 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 | EE | Kenji Yamanishi:
A Learning Criterion for Stochastic Rules.
COLT 1990: 67-81 |
