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2020 – today
- 2024
[j38]Yanjin Li, Linchuan Xu, Kenji Yamanishi:
GMMDA: Gaussian mixture modeling of graph in latent space for graph data augmentation. Knowl. Inf. Syst. 66(12): 7667-7695 (2024)- [i24]Kento Urano, Ryo Yuki, Kenji Yamanishi:
Clustering Change Sign Detection by Fusing Mixture Complexity. CoRR abs/2403.18269 (2024) - [i23]Atsushi Suzuki, Kota Fukuzawa, Kenji Yamanishi:
Foundation of Calculating Normalized Maximum Likelihood for Continuous Probability Models. CoRR abs/2409.08387 (2024) - [i22]Shintaro Fukushima, Kenji Yamanishi:
Graph Community Augmentation with GMM-based Modeling in Latent Space. CoRR abs/2412.01163 (2024) - 2023
- [b1]Kenji Yamanishi:
Learning with the Minimum Description Length Principle. Springer 2023, ISBN 978-981-99-1789-1, pp. 1-339 - [j37]Kenji Yamanishi
, So Hirai:
Detecting signs of model change with continuous model selection based on descriptive dimensionality. Appl. Intell. 53(22): 26454-26471 (2023) - [j36]Ryo Yuki, Yuichi Ike, Kenji Yamanishi:
Dimensionality selection for hyperbolic embeddings using decomposed normalized maximum likelihood code-length. Knowl. Inf. Syst. 65(12): 5601-5634 (2023) - [j35]Chuan-hao Lin, Linchuan Xu, Kenji Yamanishi:
Network Change Detection Based on Random Walk in Latent Space. IEEE Trans. Knowl. Data Eng. 35(6): 6136-6147 (2023) - [c85]Yanjin Li, Linchuan Xu, Kenji Yamanishi:
GMMDA: Gaussian Mixture Modeling of Graph in Latent Space for Graph Data Augmentation. ICDM 2023: 319-328 - [c84]Shintaro Fukushima, Kenji Yamanishi:
Balancing Summarization and Change Detection in Graph Streams. ICDM 2023: 1025-1030 - [c83]Ryo Yuki, Atsushi Suzuki, Kenji Yamanishi:
Dimensionality and Curvature Selection of Graph Embedding using Decomposed Normalized Maximum Likelihood Code-Length. ICDM 2023: 1517-1522 - [c82]Atsushi Suzuki, Atsushi Nitanda, Taiji Suzuki, Jing Wang, Feng Tian, Kenji Yamanishi:
Tight and fast generalization error bound of graph embedding in metric space. ICML 2023: 33268-33284 - [c81]Naoki Nishikawa, Yuichi Ike, Kenji Yamanishi:
Adaptive Topological Feature via Persistent Homology: Filtration Learning for Point Clouds. NeurIPS 2023 - [i21]Kenji Yamanishi, So Hirai:
Detecting Signs of Model Change with Continuous Model Selection Based on Descriptive Dimensionality. CoRR abs/2302.12127 (2023) - [i20]Atsushi Suzuki, Atsushi Nitanda, Taiji Suzuki, Jing Wang, Feng Tian, Kenji Yamanishi:
Tight and fast generalization error bound of graph embedding in metric space. CoRR abs/2305.07971 (2023) - [i19]Naoki Nishikawa, Yuichi Ike, Kenji Yamanishi:
Adaptive Topological Feature via Persistent Homology: Filtration Learning for Point Clouds. CoRR abs/2307.09259 (2023) - [i18]Shintaro Fukushima, Kenji Yamanishi:
Balancing Summarization and Change Detection in Graph Streams. CoRR abs/2311.18694 (2023) - 2022
- [j34]Shunki Kyoya, Kenji Yamanishi:
Mixture Complexity and Its Application to Gradual Clustering Change Detection. Entropy 24(10): 1407 (2022) - [c80]Ryo Yuki, Yuichi Ike, Kenji Yamanishi:
Dimensionality Selection of Hyperbolic Graph Embeddings using Decomposed Normalized Maximum Likelihood Code-Length. ICDM 2022: 666-675 - [c79]Kohei Ueda, Yuichi Ike, Kenji Yamanishi:
Change Detection with Probabilistic Models on Persistence Diagrams. ICDM 2022: 1191-1196 - 2021
- [j33]Jun Huang, Linchuan Xu, Kun Qian, Jing Wang, Kenji Yamanishi:
Multi-label learning with missing and completely unobserved labels. Data Min. Knowl. Discov. 35(3): 1061-1086 (2021) - [j32]Pham Thuc Hung, Kenji Yamanishi:
Word2vec Skip-Gram Dimensionality Selection via Sequential Normalized Maximum Likelihood. Entropy 23(8): 997 (2021) - [j31]Shunki Kyoya, Kenji Yamanishi:
Summarizing Finite Mixture Model with Overlapping Quantification. Entropy 23(11): 1503 (2021) - [j30]Atsushi Suzuki, Kenji Yamanishi:
Fourier-Analysis-Based Form of Normalized Maximum Likelihood: Exact Formula and Relation to Complex Bayesian Prior. IEEE Trans. Inf. Theory 67(9): 6164-6178 (2021) - [j29]Linchuan Xu, Jing Wang, Lifang He, Jiannong Cao, Xiaokai Wei, Philip S. Yu, Kenji Yamanishi:
MixSp: A Framework for Embedding Heterogeneous Information Networks With Arbitrary Number of Node and Edge Types. IEEE Trans. Knowl. Data Eng. 33(6): 2627-2639 (2021) - [c78]So Hirai, Kenji Yamanishi:
Detecting Gradual Structure Changes of Non-parametric Distributions via Kernel Complexity. IEEE BigData 2021: 17-27 - [c77]Shintaro Fukushima, Ryoga Kanai, Kenji Yamanishi:
Graph Summarization with Latent Variable Probabilistic Models. COMPLEX NETWORKS 2021: 428-440 - [c76]Atsushi Suzuki, Atsushi Nitanda, Jing Wang, Linchuan Xu, Kenji Yamanishi, Marc Cavazza:
Generalization Error Bound for Hyperbolic Ordinal Embedding. ICML 2021: 10011-10021 - [c75]Linchuan Xu, Ryo Asaoka, Taichi Kiwaki, Hiroshi Murata, Yuri Fujino, Kenji Yamanishi:
PAMI: A Computational Module for Joint Estimation and Progression Prediction of Glaucoma. KDD 2021: 3826-3834 - [c74]Atsushi Suzuki, Atsushi Nitanda, Jing Wang, Linchuan Xu, Kenji Yamanishi, Marc Cavazza:
Generalization Bounds for Graph Embedding Using Negative Sampling: Linear vs Hyperbolic. NeurIPS 2021: 1243-1255 - [i17]Atsushi Suzuki, Atsushi Nitanda, Jing Wang, Linchuan Xu, Marc Cavazza, Kenji Yamanishi:
Generalization Error Bound for Hyperbolic Ordinal Embedding. CoRR abs/2105.10475 (2021) - 2020
- [j28]Taito Lee, Shin Matsushima, Kenji Yamanishi:
Grafting for combinatorial binary model using frequent itemset mining. Data Min. Knowl. Discov. 34(1): 101-123 (2020) - [c73]Shintaro Fukushima, Kenji Yamanishi:
Detecting Hierarchical Changes in Latent Variable Models. ICDM 2020: 1028-1033 - [c72]Jun Huang, Linchuan Xu, Jing Wang, Lei Feng, Kenji Yamanishi:
Discovering Latent Class Labels for Multi-Label Learning. IJCAI 2020: 3058-3064 - [i16]Shunki Kyoya, Kenji Yamanishi:
Mixture Complexity and Its Application to Gradual Clustering Change Detection. CoRR abs/2007.07467 (2020) - [i15]Shintaro Fukushima, Atsushi Nitanda, Kenji Yamanishi:
Online Robust and Adaptive Learning from Data Streams. CoRR abs/2007.12160 (2020) - [i14]Kenji Yamanishi, Linchuan Xu, Ryo Yuki, Shintaro Fukushima, Chuan-hao Lin:
Change Sign Detection with Differential MDL Change Statistics and its Applications to COVID-19 Pandemic Analysis. CoRR abs/2007.15179 (2020) - [i13]Linchuan Xu, Jun Huang, Atsushi Nitanda, Ryo Asaoka, Kenji Yamanishi:
A Novel Global Spatial Attention Mechanism in Convolutional Neural Network for Medical Image Classification. CoRR abs/2007.15897 (2020) - [i12]Pham Thuc Hung, Kenji Yamanishi:
Word2vec Skip-gram Dimensionality Selection via Sequential Normalized Maximum Likelihood. CoRR abs/2008.07720 (2020) - [i11]Shintaro Fukushima, Kenji Yamanishi:
Detecting Hierarchical Changes in Latent Variable Models. CoRR abs/2011.09465 (2020)
2010 – 2019
- 2019
- [j27]Kenji Yamanishi, Tianyi Wu, Shinya Sugawara, Makoto Okada:
The decomposed normalized maximum likelihood code-length criterion for selecting hierarchical latent variable models. Data Min. Knowl. Discov. 33(4): 1017-1058 (2019) - [j26]Yunhui Fu, Shin Matsushima, Kenji Yamanishi:
Model Selection for Non-Negative Tensor Factorization with Minimum Description Length. Entropy 21(7): 632 (2019) - [j25]Shintaro Fukushima, Kenji Yamanishi:
Detecting Metachanges in Data Streams from the Viewpoint of the MDL Principle. Entropy 21(12): 1134 (2019) - [j24]So Hirai, Kenji Yamanishi:
Correction to Efficient Computation of Normalized Maximum Likelihood Codes for Gaussian Mixture Models With Its Applications to Clustering. IEEE Trans. Inf. Theory 65(10): 6827-6828 (2019) - [c71]Jing Wang, Atsushi Suzuki, Linchuan Xu, Feng Tian, Liang Yang, Kenji Yamanishi:
Orderly Subspace Clustering. AAAI 2019: 5264-5272 - [c70]Atsushi Suzuki, Jing Wang, Feng Tian, Atsushi Nitanda, Kenji Yamanishi:
Hyperbolic Ordinal Embedding. ACML 2019: 1065-1080 - [c69]Kohei Miyaguchi, Kenji Yamanishi:
Adaptive Minimax Regret against Smooth Logarithmic Losses over High-Dimensional l1-Balls via Envelope Complexity. AISTATS 2019: 3440-3448 - [c68]So Hirai, Kenji Yamanishi:
Detecting Model Changes and their Early Warning Signals Using MDL Change Statistics. IEEE BigData 2019: 84-93 - [c67]Jing Wang, Linchuan Xu, Feng Tian, Atsushi Suzuki, Changqing Zhang, Kenji Yamanishi:
Attributed Subspace Clustering. IJCAI 2019: 3719-3725 - [c66]Yuhui Zheng, Linchuan Xu, Taichi Kiwaki, Jing Wang, Hiroshi Murata, Ryo Asaoka, Kenji Yamanishi:
Glaucoma Progression Prediction Using Retinal Thickness via Latent Space Linear Regression. KDD 2019: 2278-2286 - [c65]Jilles Vreeken, Kenji Yamanishi:
Modern MDL meets Data Mining Insights, Theory, and Practice. KDD 2019: 3229-3230 - [i10]Kenji Yamanishi:
Descriptive Dimensionality and Its Characterization of MDL-based Learning and Change Detection. CoRR abs/1910.11540 (2019) - 2018
- [j23]Kohei Miyaguchi, Kenji Yamanishi:
High-dimensional penalty selection via minimum description length principle. Mach. Learn. 107(8-10): 1283-1302 (2018) - [j22]Kenji Yamanishi, Shintaro Fukushima:
Model Change Detection With the MDL Principle. IEEE Trans. Inf. Theory 64(9): 6115-6126 (2018) - [j21]Koichi Moriya, Shin Matsushima, Kenji Yamanishi:
Traffic Risk Mining From Heterogeneous Road Statistics. IEEE Trans. Intell. Transp. Syst. 19(11): 3662-3675 (2018) - [c64]So Hirai, Kenji Yamanishi:
Detecting Latent Structure Uncertainty with Structural Entropy. IEEE BigData 2018: 26-35 - [c63]Jing Wang, Feng Tian, Weiwei Liu, Xiao Wang, Wenjie Zhang, Kenji Yamanishi:
Ranking Preserving Nonnegative Matrix Factorization. IJCAI 2018: 2776-2782 - [c62]Atsushi Suzuki, Kenji Yamanishi:
Exact Calculation of Normalized Maximum Likelihood Code Length Using Fourier Analysis. ISIT 2018: 1211-1215 - [c61]Hiroki Sugiura, Taichi Kiwaki, Siamak Yousefi, Hiroshi Murata, Ryo Asaoka, Kenji Yamanishi:
Estimating Glaucomatous Visual Sensitivity from Retinal Thickness with Pattern-Based Regularization and Visualization. KDD 2018: 783-792 - [i9]Kohei Miyaguchi, Kenji Yamanishi:
High-dimensional Penalty Selection via Minimum Description Length Principle. CoRR abs/1804.09904 (2018) - [i8]Yosuke Enokida, Atsushi Suzuki, Kenji Yamanishi:
Stable Geodesic Update on Hyperbolic Space and its Application to Poincare Embeddings. CoRR abs/1805.10487 (2018) - [i7]Kohei Miyaguchi, Kenji Yamanishi:
Adaptive Minimax Regret against Smooth Logarithmic Losses over High-Dimensional ε1-Balls via Envelope Complexity. CoRR abs/1810.03825 (2018) - 2017
- [j20]Kohei Miyaguchi, Kenji Yamanishi:
Online detection of continuous changes in stochastic processes. Int. J. Data Sci. Anal. 3(3): 213-229 (2017) - [c60]Ryoya Kaneko, Kohei Miyaguchi, Kenji Yamanishi:
Detecting changes in streaming data with information-theoretic windowing. IEEE BigData 2017: 646-655 - [c59]Tatsuru Kobayashi, Shin Matsushima, Taito Lee, Kenji Yamanishi:
Discovering potential traffic risks in Japan using a supervised learning approach. IEEE BigData 2017: 948-955 - [c58]Tomohiko Nakmaura, Tomoharu Iwata, Kenji Yamanishi:
Latent Dimensionality Estimation for Probabilistic Canonical Correlation Analysis Using Normalized Maximum Likelihood Code-Length. DSAA 2017: 716-725 - [c57]Tianyi Wu, Shinya Sugawara, Kenji Yamanishi:
Decomposed Normalized Maximum Likelihood Codelength Criterion for Selecting Hierarchical Latent Variable Models. KDD 2017: 1165-1174 - [c56]Toshimitsu Uesaka, Kai Morino, Hiroki Sugiura, Taichi Kiwaki, Hiroshi Murata, Ryo Asaoka, Kenji Yamanishi:
Multi-view Learning over Retinal Thickness and Visual Sensitivity on Glaucomatous Eyes. KDD 2017: 2041-2050 - [c55]Kohei Miyaguchi, Shin Matsushima, Kenji Yamanishi:
Sparse Graphical Modeling via Stochastic Complexity. SDM 2017: 723-731 - [i6]So Hirai, Kenji Yamanishi:
An Upper Bound on Normalized Maximum Likelihood Codes for Gaussian Mixture Models. CoRR abs/1709.00925 (2017) - [i5]Taito Lee, Shin Matsushima, Kenji Yamanishi:
Grafting for Combinatorial Boolean Model using Frequent Itemset Mining. CoRR abs/1711.02478 (2017) - 2016
- [c54]Kenji Yamanishi, Kohei Miyaguchi:
Detecting gradual changes from data stream using MDL-change statistics. IEEE BigData 2016: 156-163 - [c53]Kyosuke Tomoda, Kai Morino, Hiroshi Murata, Ryo Asaoka, Kenji Yamanishi:
Predicting Glaucomatous Progression with Piecewise Regression Model from Heterogeneous Medical Data. HEALTHINF 2016: 93-104 - [c52]Yoshitaro Yonamoto, Kai Morino, Kenji Yamanishi:
Temporal Network Change Detection Using Network Centralities. DSAA 2016: 51-60 - [c51]Akihiro Demachi, Shin Matsushima, Kenji Yamanishi:
Web Behavior Analysis Using Sparse Non-Negative Matrix Factorization. DSAA 2016: 574-583 - [c50]Taito Lee, Shin Matsushima, Kenji Yamanishi:
Traffic Risk Mining Using Partially Ordered Non-Negative Matrix Factorization. DSAA 2016: 622-631 - [c49]Atsushi Suzuki, Kohei Miyaguchi, Kenji Yamanishi:
Structure Selection for Convolutive Non-negative Matrix Factorization Using Normalized Maximum Likelihood Coding. ICDM 2016: 1221-1226 - [c48]Yu Ito, Shinichi Oeda, Kenji Yamanishi:
Rank Selection for Non-negative Matrix Factorization with Normalized Maximum Likelihood Coding. SDM 2016: 720-728 - [i4]Motohide Higaki, Kai Morino, Hiroshi Murata, Ryo Asaoka, Kenji Yamanishi:
Predicting Glaucoma Visual Field Loss by Hierarchically Aggregating Clustering-based Predictors. CoRR abs/1603.07094 (2016) - 2015
- [j19]Yu Hayashi, Kenji Yamanishi:
Sequential network change detection with its applications to ad impact relation analysis. Data Min. Knowl. Discov. 29(1): 137-167 (2015) - [j18]Shota Saito, Ryota Tomioka, Kenji Yamanishi:
Early detection of persistent topics in social networks. Soc. Netw. Anal. Min. 5(1): 19:1-19:15 (2015) - [c47]Kohei Miyaguchi, Kenji Yamanishi:
On-line detection of continuous changes in stochastic processes. DSAA 2015: 1-9 - [c46]Koichi Moriya, Shin Matsushima, Kenji Yamanishi:
Traffic risk mining from heterogeneous road statistics. DSAA 2015: 1-10 - [c45]Shigeru Maya, Kai Morino, Hiroshi Murata, Ryo Asaoka, Kenji Yamanishi:
Discovery of Glaucoma Progressive Patterns Using Hierarchical MDL-Based Clustering. KDD 2015: 1979-1988 - 2014
- [j17]Toshimitsu Takahashi, Ryota Tomioka, Kenji Yamanishi:
Discovering Emerging Topics in Social Streams via Link-Anomaly Detection. IEEE Trans. Knowl. Data Eng. 26(1): 120-130 (2014) - [c44]Shota Saito, Ryota Tomioka, Kenji Yamanishi:
Early detection of persistent topics in social networks. ASONAM 2014: 417-424 - [c43]Shigeru Maya, Kai Morino, Kenji Yamanishi:
Predicting glaucoma progression using multi-task learning with heterogeneous features. IEEE BigData 2014: 261-270 - [c42]Shinichi Oeda, Yu Ito, Kenji Yamanishi:
Extracting Latent Skills from Time Series of Asynchronous and Incomplete Examinations. EDM 2014: 367-368 - [c41]Yoshiki Sakai, Kenji Yamanishi:
Data Fusion Using Restricted Boltzmann Machines. ICDM 2014: 953-958 - 2013
- [j16]So Hirai, Kenji Yamanishi:
Efficient Computation of Normalized Maximum Likelihood Codes for Gaussian Mixture Models With Its Applications to Clustering. IEEE Trans. Inf. Theory 59(11): 7718-7727 (2013) - [c40]Yoshiki Sakai, Kenji Yamanishi:
An NML-based model selection criterion for general relational data modeling. IEEE BigData 2013: 421-429 - [c39]Shinichi Oeda, Kenji Yamanishi:
Extracting Time-evolving Latent Skills from Examination Time Series. EDM 2013: 340-341 - [c38]Zenghan Liang, Ryota Tomioka, Hiroshi Murata, Ryo Asaoka, Kenji Yamanishi:
Quantitative Prediction of Glaucomatous Visual Field Loss from Few Measurements. ICDM 2013: 1121-1126 - [c37]Sho-Ichi Sato, Kenji Yamanishi:
Graph Partitioning Change Detection Using Tree-Based Clustering. ICDM 2013: 1169-1174 - 2012
- [c36]Yu Hayashi, Kenji Yamanishi:
Sequential Network Change Detection with Its Applications to Ad Impact Relation Analysis. ICDM 2012: 280-289 - [c35]Eiichi Sakurai, Kenji Yamanishi:
Comparison of dynamic model selection with infinite HMM for statistical model change detection. ITW 2012: 302-306 - [c34]Hiroki Kanazawa, Kenji Yamanishi:
An MDL-based change-detection algorithm with its applications to learning piecewise stationary memoryless sources. ITW 2012: 557-561 - [c33]So Hirai, Kenji Yamanishi:
Detecting changes of clustering structures using normalized maximum likelihood coding. KDD 2012: 343-351 - [i3]So Hirai, Kenji Yamanishi:
Normalized Maximum Likelihood Coding for Exponential Family with Its Applications to Optimal Clustering. CoRR abs/1205.3549 (2012) - 2011
- [c32]Toshimitsu Takahashi, Ryota Tomioka, Kenji Yamanishi:
Discovering Emerging Topics in Social Streams via Link Anomaly Detection. ICDM 2011: 1230-1235 - [c31]So Hirai, Kenji Yamanishi:
Efficient computation of normalized maximum likelihood coding for Gaussian mixtures with its applications to optimal clustering. ISIT 2011: 1031-1035 - [c30]Yasuhiro Urabe, Kenji Yamanishi, Ryota Tomioka, Hiroki Iwai:
Real-Time Change-Point Detection Using Sequentially Discounting Normalized Maximum Likelihood Coding. PAKDD (2) 2011: 185-197 - [i2]Toshimitsu Takahashi, Ryota Tomioka, Kenji Yamanishi:
Discovering Emerging Topics in Social Streams via Link Anomaly Detection. CoRR abs/1110.2899 (2011)
2000 – 2009
- 2009
- [j15]Ryohei Fujimaki, Takayuki Nakata, Hidenori Tsukahara, Akinori Sato, Kenji Yamanishi:
Mining abnormal patterns from heterogeneous time-series with irrelevant features for fault event detection. Stat. Anal. Data Min. 2(1): 1-17 (2009) - [j14]Shunsuke Hirose, Kenji Yamanishi:
Latent variable mining with its applications to anomalous behavior detection. Stat. Anal. Data Min. 2(1): 70-86 (2009) - [c29]Shunsuke Hirose, Kenji Yamanishi, Takayuki Nakata, Ryohei Fujimaki:
Network anomaly detection based on Eigen equation compression. KDD 2009: 1185-1194 - 2008
- [c28]Shunsuke Hirose, Kenji Yamanishi:
Latent Variable Mining with Its Applications to Anomalous Behavior Detection. SDM 2008: 231-242 - 2007
- [j13]Kenji Yamanishi, Yuko Maruyama:
Dynamic Model Selection With its Applications to Novelty Detection. IEEE Trans. Inf. Theory 53(6): 2180-2189 (2007) - 2006
- [j12]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
- [c27]Kenji Yamanishi, Yuko Maruyama:
Dynamic syslog mining for network failure monitoring. KDD 2005: 499-508 - 2004
- [j11]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) - [c26]Yuko Maruyama, Kenji Yamanishi:
Dynamic model selection with its applications to computer security. ITW 2004: 82-87 - [c25]Satoshi Morinaga, Kenji Yamanishi:
Tracking dynamics of topic trends using a finite mixture model. KDD 2004: 811-816 - 2003
- [j10]Hang Li, Kenji Yamanishi:
Topic analysis using a finite mixture model. Inf. Process. Manag. 39(4): 521-541 (2003) - [c24]Satoshi Morinaga, Kenji Yamanishi, Jun'ichi Takeuchi:
Distributed cooperative mining for information consortia. KDD 2003: 619-624 - 2002
- [j9]Kenji Yamanishi, Hang Li:
Mining Open Answers in Questionnaire Data. IEEE Intell. Syst. 17(5): 58-63 (2002) - [j8]Hang Li, Kenji Yamanishi:
Text classification using ESC-based stochastic decision lists. Inf. Process. Manag. 38(3): 343-361 (2002) - [c23]Satoshi Morinaga, Kenji Yamanishi, Kenji Tateishi, Toshikazu Fukushima:
Mining product reputations on the Web. KDD 2002: 341-349 - [c22]Kenji Yamanishi, Jun'ichi Takeuchi:
A unifying framework for detecting outliers and change points from non-stationary time series data. KDD 2002: 676-681 - 2001
- [c21]Kenji Yamanishi, Jun'ichi Takeuchi:
Discovering outlier filtering rules from unlabeled data: combining a supervised learner with an unsupervised learner. KDD 2001: 389-394 - [c20]Hang Li, Kenji Yamanishi:
Mining from open answers in questionnaire data. KDD 2001: 443-449 - 2000
- [c19]Hang Li, Kenji Yamanishi:
Topic Analysis Using a Finite Mixture Model. EMNLP 2000: 35-44 - [c18]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
1990 – 1999
- 1999
- [j7]Kenji Yamanishi:
Distributed Cooperative Bayesian Learning Strategies. Inf. Comput. 150(1): 22-56 (1999) - [c17]Kenji Yamanishi:
Extended Stochastic Complexity and Minimax Relative Loss Analysis. ALT 1999: 26-38 - [c16]Hang Li, Kenji Yamanishi:
Text Classification Using ESC-based Stochastic Decision Lists. CIKM 1999: 122-130 - 1998
- [j6]Kenji Yamanishi:
A Decision-Theoretic Extension of Stochastic Complexity and Its Applications to Learning. IEEE Trans. Inf. Theory 44(4): 1424-1439 (1998) - [c15]Kenji Yamanishi:
Minimax Relative Loss Analysis for Sequential Prediction Algorithms Using Parametric Hypotheses. COLT 1998: 32-43 - 1997
- [j5]Kenji Yamanishi:
On-Line Maximum Likelihood Prediction with Respect to General Loss Functions. J. Comput. Syst. Sci. 55(1): 105-118 (1997) - [c14]Hang Li, Kenji Yamanishi:
Document Classification Using a Finite Mixture Model. ACL 1997: 39-47 - [c13]Kenji Yamanishi:
Distributed Cooperative Bayesian Learning Strategies. COLT 1997: 250-262 - [i1]Hang Li, Kenji Yamanishi:
Document Classification Using a Finite Mixture Model. CoRR cmp-lg/9705005 (1997) - 1996
- [c12]Kenji Yamanishi:
A Randomized Approximation of the MDL for Stochastic Models with Hidden Variables. COLT 1996: 99-109 - 1995
- [j4]Hiroshi Mamitsuka, Kenji Yamanishi:
alpha-Helix region prediction with stochastic rule learning. Comput. Appl. Biosci. 11(4): 399-411 (1995) - [j3]Kenji Yamanishi:
A Loss Bound Model for On-Line Stochastic Prediction Algorithms. Inf. Comput. 119(1): 39-54 (1995) - [j2]Kenji Yamanishi:
Probably Almost Discriminative Learning. Mach. Learn. 18(1): 23-50 (1995) - [c11]Kenji Yamanishi:
Randomized Approximate Aggregating Strategies and Their Applications to Prediction and Discrimination. COLT 1995: 83-90 - [c10]Kenji Yamanishi:
On-line maximum likelihood prediction with respect to general loss functions. EuroCOLT 1995: 84-98 - 1994
- [c9]Kenji Yamanishi:
The Minimum L-Complexity Algorithm and its Applications to Learning Non-Parametric Rules. COLT 1994: 173-182 - 1993
- [c8]Kenji Yamanishi:
On Polynomial-Time Probably almost Discriminative Learnability. COLT 1993: 94-100 - [c7]Kenji Yamanishi:
Learning non-parametric smooth rules by stochastic rules with finite partitioning. EuroCOLT 1993: 217-227 - 1992
- [j1]Kenji Yamanishi:
A Learning Criterion for Stochastic Rules. Mach. Learn. 9: 165-203 (1992) - [c6]Hiroshi Mamitsuka, Kenji Yamanishi:
Protein Secondary Structure Prediction Based on Stochastic-Rule Learning. ALT 1992: 240-251 - [c5]Kenji Yamanishi:
Probably Almost Discriminative Learning. COLT 1992: 164-171 - 1991
- [c4]Kenji Yamanishi:
Learning non-parametric densities by finite-dimensional parametric hypotheses. ALT 1991: 175-186 - [c3]Kenji Yamanishi:
A Loss Bound Model for On-Line Stochastic Prediction Strategies. COLT 1991: 290-302 - [c2]Kenji Yamanishi, Akihiko Konagaya:
Learning Stochastic Motifs from Genetic Sequences. ML 1991: 467-471 - 1990
- [c1]Kenji Yamanishi:
A Learning Criterion for Stochastic Rules. COLT 1990: 67-81
Coauthor Index
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