Kenji Yamanishi

153 papers A* 49A 5B 18C 1Journal 65Unranked 14

Year	Rank	Type	Title / Venue / Authors
2026	J	`jnl`	Data- and Variance-dependent Regret Bounds for Online Tabular MDPs. CoRR Mingyi Li, Taira Tsuchiya, Kenji Yamanishi
2025	J	`jnl`	Bandit and Delayed Feedback in Online Structured Prediction. CoRR Yuki Shibukawa, Taira Tsuchiya, Shinsaku Sakaue, Kenji Yamanishi
2025	J	`jnl`	Normalized Maximum Likelihood Code-Length on Riemannian Manifold Data Spaces. CoRR Kota Fukuzawa, Atsushi Suzuki, Kenji Yamanishi
2024	J	`jnl`	Clustering Change Sign Detection by Fusing Mixture Complexity. CoRR Kento Urano, Ryo Yuki, Kenji Yamanishi
2024	J	`jnl`	Foundation of Calculating Normalized Maximum Likelihood for Continuous Probability Models. CoRR Atsushi Suzuki, Kota Fukuzawa, Kenji Yamanishi
2024	J	`jnl`	GMMDA: Gaussian mixture modeling of graph in latent space for graph data augmentation. Knowl. Inf. Syst. Yanjin Li, Linchuan Xu, Kenji Yamanishi
2024	A*	`conf`	Graph Community Augmentation with GMM-Based Modeling in Latent Space. ICDM Shintaro Fukushima, Kenji Yamanishi
2024	J	`jnl`	Graph Community Augmentation with GMM-based Modeling in Latent Space. CoRR Shintaro Fukushima, Kenji Yamanishi
2024	B	`conf`	Luckiness Normalized Maximum Likelihood-based Change Detection for High-dimensional Graphical Models with Missing Data. IEEE Big Data Zhongyi Que, Linchuan Xu, Kenji Yamanishi
2023	A*	`conf`	Adaptive Topological Feature via Persistent Homology: Filtration Learning for Point Clouds. NeurIPS Naoki Nishikawa, Yuichi Ike, Kenji Yamanishi
2023	J	`jnl`	Adaptive Topological Feature via Persistent Homology: Filtration Learning for Point Clouds. CoRR Naoki Nishikawa, Yuichi Ike, Kenji Yamanishi
2023	A*	`conf`	Balancing Summarization and Change Detection in Graph Streams. ICDM Shintaro Fukushima, Kenji Yamanishi
2023	J	`jnl`	Balancing Summarization and Change Detection in Graph Streams. CoRR Shintaro Fukushima, Kenji Yamanishi
2023	J	`jnl`	Detecting Signs of Model Change with Continuous Model Selection Based on Descriptive Dimensionality. CoRR Kenji Yamanishi, So Hirai
2023	J	`jnl`	Detecting signs of model change with continuous model selection based on descriptive dimensionality. Appl. Intell. Kenji Yamanishi, So Hirai
2023	A*	`conf`	Dimensionality and Curvature Selection of Graph Embedding using Decomposed Normalized Maximum Likelihood Code-Length. ICDM Ryo Yuki, Atsushi Suzuki, Kenji Yamanishi
2023	J	`jnl`	Dimensionality selection for hyperbolic embeddings using decomposed normalized maximum likelihood code-length. Knowl. Inf. Syst. Ryo Yuki, Yuichi Ike, Kenji Yamanishi
2023	A*	`conf`	GMMDA: Gaussian Mixture Modeling of Graph in Latent Space for Graph Data Augmentation. ICDM Yanjin Li, Linchuan Xu, Kenji Yamanishi
2023	—	`book`	Learning with the Minimum Description Length Principle Kenji Yamanishi
2023	J	`jnl`	Network Change Detection Based on Random Walk in Latent Space. IEEE Trans. Knowl. Data Eng. Chuan-hao Lin, Linchuan Xu, Kenji Yamanishi
2023	A*	`conf`	Tight and fast generalization error bound of graph embedding in metric space. ICML Atsushi Suzuki, Atsushi Nitanda, Taiji Suzuki, Jing Wang, Feng Tian, Kenji Yamanishi
2023	J	`jnl`	Tight and fast generalization error bound of graph embedding in metric space. CoRR Atsushi Suzuki, Atsushi Nitanda, Taiji Suzuki, Jing Wang, Feng Tian, Kenji Yamanishi
2022	A*	`conf`	Change Detection with Probabilistic Models on Persistence Diagrams. ICDM Kohei Ueda, Yuichi Ike, Kenji Yamanishi
2022	A*	`conf`	Dimensionality Selection of Hyperbolic Graph Embeddings using Decomposed Normalized Maximum Likelihood Code-Length. ICDM Ryo Yuki, Yuichi Ike, Kenji Yamanishi
2022	J	`jnl`	Mixture Complexity and Its Application to Gradual Clustering Change Detection. Entropy Shunki Kyoya, Kenji Yamanishi
2021	—	`conf`	Detecting Gradual Structure Changes of Non-parametric Distributions via Kernel Complexity. IEEE BigData So Hirai, Kenji Yamanishi
2021	J	`jnl`	Fourier-Analysis-Based Form of Normalized Maximum Likelihood: Exact Formula and Relation to Complex Bayesian Prior. IEEE Trans. Inf. Theory Atsushi Suzuki, Kenji Yamanishi
2021	A*	`conf`	Generalization Bounds for Graph Embedding Using Negative Sampling: Linear vs Hyperbolic. NeurIPS Atsushi Suzuki, Atsushi Nitanda, Jing Wang, Linchuan Xu, Kenji Yamanishi, Marc Cavazza
2021	A*	`conf`	Generalization Error Bound for Hyperbolic Ordinal Embedding. ICML Atsushi Suzuki, Atsushi Nitanda, Jing Wang, Linchuan Xu, Kenji Yamanishi, Marc Cavazza
2021	J	`jnl`	Generalization Error Bound for Hyperbolic Ordinal Embedding. CoRR Atsushi Suzuki, Atsushi Nitanda, Jing Wang, Linchuan Xu, Marc Cavazza, Kenji Yamanishi
2021	—	`conf`	Graph Summarization with Latent Variable Probabilistic Models. COMPLEX NETWORKS Shintaro Fukushima, Ryoga Kanai, Kenji Yamanishi
2021	J	`jnl`	MixSp: A Framework for Embedding Heterogeneous Information Networks With Arbitrary Number of Node and Edge Types. IEEE Trans. Knowl. Data Eng. Linchuan Xu, Jing Wang, Lifang He, Jiannong Cao, Xiaokai Wei, Philip S. Yu, Kenji Yamanishi
2021	J	`jnl`	Multi-label learning with missing and completely unobserved labels. Data Min. Knowl. Discov. Jun Huang, Linchuan Xu, Kun Qian, Jing Wang, Kenji Yamanishi
2021	A*	`conf`	PAMI: A Computational Module for Joint Estimation and Progression Prediction of Glaucoma. KDD Linchuan Xu, Ryo Asaoka, Taichi Kiwaki, Hiroshi Murata, Yuri Fujino, Kenji Yamanishi
2021	J	`jnl`	Summarizing Finite Mixture Model with Overlapping Quantification. Entropy Shunki Kyoya, Kenji Yamanishi
2021	J	`jnl`	Word2vec Skip-Gram Dimensionality Selection via Sequential Normalized Maximum Likelihood. Entropy Pham Thuc Hung, Kenji Yamanishi
2020	J	`jnl`	A Novel Global Spatial Attention Mechanism in Convolutional Neural Network for Medical Image Classification. CoRR Linchuan Xu, Jun Huang, Atsushi Nitanda, Ryo Asaoka, Kenji Yamanishi
2020	J	`jnl`	Change Sign Detection with Differential MDL Change Statistics and its Applications to COVID-19 Pandemic Analysis. CoRR Kenji Yamanishi, Linchuan Xu, Ryo Yuki, Shintaro Fukushima, Chuan-hao Lin
2020	A*	`conf`	Detecting Hierarchical Changes in Latent Variable Models. ICDM Shintaro Fukushima, Kenji Yamanishi
2020	J	`jnl`	Detecting Hierarchical Changes in Latent Variable Models. CoRR Shintaro Fukushima, Kenji Yamanishi
2020	A*	`conf`	Discovering Latent Class Labels for Multi-Label Learning. IJCAI Jun Huang, Linchuan Xu, Jing Wang, Lei Feng, Kenji Yamanishi
2020	J	`jnl`	Grafting for combinatorial binary model using frequent itemset mining. Data Min. Knowl. Discov. Taito Lee, Shin Matsushima, Kenji Yamanishi
2020	J	`jnl`	Mixture Complexity and Its Application to Gradual Clustering Change Detection. CoRR Shunki Kyoya, Kenji Yamanishi
2020	J	`jnl`	Online Robust and Adaptive Learning from Data Streams. CoRR Shintaro Fukushima, Atsushi Nitanda, Kenji Yamanishi
2020	J	`jnl`	Word2vec Skip-gram Dimensionality Selection via Sequential Normalized Maximum Likelihood. CoRR Pham Thuc Hung, Kenji Yamanishi
2019	A	`conf`	Adaptive Minimax Regret against Smooth Logarithmic Losses over High-Dimensional l1-Balls via Envelope Complexity. AISTATS Kohei Miyaguchi, Kenji Yamanishi
2019	A*	`conf`	Attributed Subspace Clustering. IJCAI Jing Wang, Linchuan Xu, Feng Tian, Atsushi Suzuki, Changqing Zhang, Kenji Yamanishi
2019	J	`jnl`	Correction to Efficient Computation of Normalized Maximum Likelihood Codes for Gaussian Mixture Models With Its Applications to Clustering. IEEE Trans. Inf. Theory So Hirai, Kenji Yamanishi
2019	J	`jnl`	Descriptive Dimensionality and Its Characterization of MDL-based Learning and Change Detection. CoRR Kenji Yamanishi
2019	J	`jnl`	Detecting Metachanges in Data Streams from the Viewpoint of the MDL Principle. Entropy Shintaro Fukushima, Kenji Yamanishi
2019	—	`conf`	Detecting Model Changes and their Early Warning Signals Using MDL Change Statistics. IEEE BigData So Hirai, Kenji Yamanishi
2019	A*	`conf`	Glaucoma Progression Prediction Using Retinal Thickness via Latent Space Linear Regression. KDD Yuhui Zheng, Linchuan Xu, Taichi Kiwaki, Jing Wang, Hiroshi Murata, Ryo Asaoka, Kenji Yamanishi
2019	C	`conf`	Hyperbolic Ordinal Embedding. ACML Atsushi Suzuki, Jing Wang, Feng Tian, Atsushi Nitanda, Kenji Yamanishi
2019	J	`jnl`	Model Selection for Non-Negative Tensor Factorization with Minimum Description Length. Entropy Yunhui Fu, Shin Matsushima, Kenji Yamanishi
2019	A*	`conf`	Modern MDL meets Data Mining Insights, Theory, and Practice. KDD Jilles Vreeken, Kenji Yamanishi
2019	A*	`conf`	Orderly Subspace Clustering. AAAI Jing Wang, Atsushi Suzuki, Linchuan Xu, Feng Tian, Liang Yang, Kenji Yamanishi
2019	J	`jnl`	The decomposed normalized maximum likelihood code-length criterion for selecting hierarchical latent variable models. Data Min. Knowl. Discov. Kenji Yamanishi, Tianyi Wu, Shinya Sugawara, Makoto Okada
2018	J	`jnl`	Adaptive Minimax Regret against Smooth Logarithmic Losses over High-Dimensional ε CoRR Kohei Miyaguchi, Kenji Yamanishi
2018	—	`conf`	Detecting Latent Structure Uncertainty with Structural Entropy. IEEE BigData So Hirai, Kenji Yamanishi
2018	A*	`conf`	Estimating Glaucomatous Visual Sensitivity from Retinal Thickness with Pattern-Based Regularization and Visualization. KDD Hiroki Sugiura, Taichi Kiwaki, Siamak Yousefi, Hiroshi Murata, Ryo Asaoka, Kenji Yamanishi
2018	B	`conf`	Exact Calculation of Normalized Maximum Likelihood Code Length Using Fourier Analysis. ISIT Atsushi Suzuki, Kenji Yamanishi
2018	J	`jnl`	High-dimensional Penalty Selection via Minimum Description Length Principle. CoRR Kohei Miyaguchi, Kenji Yamanishi
2018	J	`jnl`	High-dimensional penalty selection via minimum description length principle. Mach. Learn. Kohei Miyaguchi, Kenji Yamanishi
2018	J	`jnl`	Model Change Detection With the MDL Principle. IEEE Trans. Inf. Theory Kenji Yamanishi, Shintaro Fukushima
2018	A*	`conf`	Ranking Preserving Nonnegative Matrix Factorization. IJCAI Jing Wang, Feng Tian, Weiwei Liu, Xiao Wang, Wenjie Zhang, Kenji Yamanishi
2018	J	`jnl`	Stable Geodesic Update on Hyperbolic Space and its Application to Poincare Embeddings. CoRR Yosuke Enokida, Atsushi Suzuki, Kenji Yamanishi
2018	J	`jnl`	Traffic Risk Mining From Heterogeneous Road Statistics. IEEE Trans. Intell. Transp. Syst. Koichi Moriya, Shin Matsushima, Kenji Yamanishi
2017	J	`jnl`	An Upper Bound on Normalized Maximum Likelihood Codes for Gaussian Mixture Models. CoRR So Hirai, Kenji Yamanishi
2017	A*	`conf`	Decomposed Normalized Maximum Likelihood Codelength Criterion for Selecting Hierarchical Latent Variable Models. KDD Tianyi Wu, Shinya Sugawara, Kenji Yamanishi
2017	—	`conf`	Detecting changes in streaming data with information-theoretic windowing. IEEE BigData Ryoya Kaneko, Kohei Miyaguchi, Kenji Yamanishi
2017	—	`conf`	Discovering potential traffic risks in Japan using a supervised learning approach. IEEE BigData Tatsuru Kobayashi, Shin Matsushima, Taito Lee, Kenji Yamanishi
2017	J	`jnl`	Grafting for Combinatorial Boolean Model using Frequent Itemset Mining. CoRR Taito Lee, Shin Matsushima, Kenji Yamanishi
2017	B	`conf`	Latent Dimensionality Estimation for Probabilistic Canonical Correlation Analysis Using Normalized Maximum Likelihood Code-Length. DSAA Tomohiko Nakmaura, Tomoharu Iwata, Kenji Yamanishi
2017	A*	`conf`	Multi-view Learning over Retinal Thickness and Visual Sensitivity on Glaucomatous Eyes. KDD Toshimitsu Uesaka, Kai Morino, Hiroki Sugiura, Taichi Kiwaki, Hiroshi Murata, Ryo Asaoka, Kenji Yamanishi
2017	J	`jnl`	Online detection of continuous changes in stochastic processes. Int. J. Data Sci. Anal. Kohei Miyaguchi, Kenji Yamanishi
2017	A	`conf`	Sparse Graphical Modeling via Stochastic Complexity. SDM Kohei Miyaguchi, Shin Matsushima, Kenji Yamanishi
2016	—	`conf`	Detecting gradual changes from data stream using MDL-change statistics. IEEE BigData Kenji Yamanishi, Kohei Miyaguchi
2016	J	`jnl`	Predicting Glaucoma Visual Field Loss by Hierarchically Aggregating Clustering-based Predictors. CoRR Motohide Higaki, Kai Morino, Hiroshi Murata, Ryo Asaoka, Kenji Yamanishi
2016	—	`conf`	Predicting Glaucomatous Progression with Piecewise Regression Model from Heterogeneous Medical Data. HEALTHINF Kyosuke Tomoda, Kai Morino, Hiroshi Murata, Ryo Asaoka, Kenji Yamanishi
2016	A	`conf`	Rank Selection for Non-negative Matrix Factorization with Normalized Maximum Likelihood Coding. SDM Yu Ito, Shinichi Oeda, Kenji Yamanishi
2016	A*	`conf`	Structure Selection for Convolutive Non-negative Matrix Factorization Using Normalized Maximum Likelihood Coding. ICDM Atsushi Suzuki, Kohei Miyaguchi, Kenji Yamanishi
2016	B	`conf`	Temporal Network Change Detection Using Network Centralities. DSAA Yoshitaro Yonamoto, Kai Morino, Kenji Yamanishi
2016	B	`conf`	Traffic Risk Mining Using Partially Ordered Non-Negative Matrix Factorization. DSAA Taito Lee, Shin Matsushima, Kenji Yamanishi
2016	B	`conf`	Web Behavior Analysis Using Sparse Non-Negative Matrix Factorization. DSAA Akihiro Demachi, Shin Matsushima, Kenji Yamanishi
2015	A*	`conf`	Discovery of Glaucoma Progressive Patterns Using Hierarchical MDL-Based Clustering. KDD Shigeru Maya, Kai Morino, Hiroshi Murata, Ryo Asaoka, Kenji Yamanishi
2015	J	`jnl`	Early detection of persistent topics in social networks. Soc. Netw. Anal. Min. Shota Saito, Ryota Tomioka, Kenji Yamanishi
2015	B	`conf`	On-line detection of continuous changes in stochastic processes. DSAA Kohei Miyaguchi, Kenji Yamanishi
2015	J	`jnl`	Sequential network change detection with its applications to ad impact relation analysis. Data Min. Knowl. Discov. Yu Hayashi, Kenji Yamanishi
2015	B	`conf`	Traffic risk mining from heterogeneous road statistics. DSAA Koichi Moriya, Shin Matsushima, Kenji Yamanishi
2014	A*	`conf`	Data Fusion Using Restricted Boltzmann Machines. ICDM Yoshiki Sakai, Kenji Yamanishi
2014	J	`jnl`	Discovering Emerging Topics in Social Streams via Link-Anomaly Detection. IEEE Trans. Knowl. Data Eng. Toshimitsu Takahashi, Ryota Tomioka, Kenji Yamanishi
2014	B	`conf`	Early detection of persistent topics in social networks. ASONAM Shota Saito, Ryota Tomioka, Kenji Yamanishi
2014	B	`conf`	Extracting Latent Skills from Time Series of Asynchronous and Incomplete Examinations. EDM Shinichi Oeda, Yu Ito, Kenji Yamanishi
2014	—	`conf`	Predicting glaucoma progression using multi-task learning with heterogeneous features. IEEE BigData Shigeru Maya, Kai Morino, Kenji Yamanishi
2013	—	`conf`	An NML-based model selection criterion for general relational data modeling. IEEE BigData Yoshiki Sakai, Kenji Yamanishi
2013	J	`jnl`	Efficient Computation of Normalized Maximum Likelihood Codes for Gaussian Mixture Models With Its Applications to Clustering. IEEE Trans. Inf. Theory So Hirai, Kenji Yamanishi
2013	B	`conf`	Extracting Time-evolving Latent Skills from Examination Time Series. EDM Shinichi Oeda, Kenji Yamanishi
2013	A*	`conf`	Graph Partitioning Change Detection Using Tree-Based Clustering. ICDM Sho-Ichi Sato, Kenji Yamanishi
2013	A*	`conf`	Quantitative Prediction of Glaucomatous Visual Field Loss from Few Measurements. ICDM Zenghan Liang, Ryota Tomioka, Hiroshi Murata, Ryo Asaoka, Kenji Yamanishi
2012	B	`conf`	An MDL-based change-detection algorithm with its applications to learning piecewise stationary memoryless sources. ITW Hiroki Kanazawa, Kenji Yamanishi
2012	B	`conf`	Comparison of dynamic model selection with infinite HMM for statistical model change detection. ITW Eiichi Sakurai, Kenji Yamanishi
2012	A*	`conf`	Detecting changes of clustering structures using normalized maximum likelihood coding. KDD So Hirai, Kenji Yamanishi
2012	J	`jnl`	Normalized Maximum Likelihood Coding for Exponential Family with Its Applications to Optimal Clustering CoRR So Hirai, Kenji Yamanishi
2012	A*	`conf`	Sequential Network Change Detection with Its Applications to Ad Impact Relation Analysis. ICDM Yu Hayashi, Kenji Yamanishi
2011	J	`jnl`	Discovering Emerging Topics in Social Streams via Link Anomaly Detection CoRR Toshimitsu Takahashi, Ryota Tomioka, Kenji Yamanishi
2011	A*	`conf`	Discovering Emerging Topics in Social Streams via Link Anomaly Detection. ICDM Toshimitsu Takahashi, Ryota Tomioka, Kenji Yamanishi
2011	B	`conf`	Efficient computation of normalized maximum likelihood coding for Gaussian mixtures with its applications to optimal clustering. ISIT So Hirai, Kenji Yamanishi
2011	—	`conf`	Real-Time Change-Point Detection Using Sequentially Discounting Normalized Maximum Likelihood Coding. PAKDD (2) Yasuhiro Urabe, Kenji Yamanishi, Ryota Tomioka, Hiroki Iwai
2009	J	`jnl`	Latent variable mining with its applications to anomalous behavior detection. Stat. Anal. Data Min. Shunsuke Hirose, Kenji Yamanishi
2009	J	`jnl`	Mining abnormal patterns from heterogeneous time-series with irrelevant features for fault event detection. Stat. Anal. Data Min. Ryohei Fujimaki, Takayuki Nakata, Hidenori Tsukahara, Akinori Sato, Kenji Yamanishi
2009	A*	`conf`	Network anomaly detection based on Eigen equation compression. KDD Shunsuke Hirose, Kenji Yamanishi, Takayuki Nakata, Ryohei Fujimaki
2008	A	`conf`	Latent Variable Mining with Its Applications to Anomalous Behavior Detection. SDM Shunsuke Hirose, Kenji Yamanishi
2007	J	`jnl`	Dynamic Model Selection With its Applications to Novelty Detection. IEEE Trans. Inf. Theory Kenji Yamanishi, Yuko Maruyama
2006	J	`jnl`	A Unifying Framework for Detecting Outliers and Change Points from Time Series. IEEE Trans. Knowl. Data Eng. Jun'ichi Takeuchi, Kenji Yamanishi
2005	A*	`conf`	Dynamic syslog mining for network failure monitoring. KDD Kenji Yamanishi, Yuko Maruyama
2004	B	`conf`	Dynamic model selection with its applications to computer security. ITW Yuko Maruyama, Kenji Yamanishi
2004	J	`jnl`	On-Line Unsupervised Outlier Detection Using Finite Mixtures with Discounting Learning Algorithms. Data Min. Knowl. Discov. Kenji Yamanishi, Jun'ichi Takeuchi, Graham J. Williams, Peter Milne
2004	A*	`conf`	Tracking dynamics of topic trends using a finite mixture model. KDD Satoshi Morinaga, Kenji Yamanishi
2003	A*	`conf`	Distributed cooperative mining for information consortia. KDD Satoshi Morinaga, Kenji Yamanishi, Jun'ichi Takeuchi
2003	J	`jnl`	Topic analysis using a finite mixture model. Inf. Process. Manag. Hang Li, Kenji Yamanishi
2002	A*	`conf`	A unifying framework for detecting outliers and change points from non-stationary time series data. KDD Kenji Yamanishi, Jun'ichi Takeuchi
2002	J	`jnl`	Mining Open Answers in Questionnaire Data. IEEE Intell. Syst. Kenji Yamanishi, Hang Li
2002	A*	`conf`	Mining product reputations on the Web. KDD Satoshi Morinaga, Kenji Yamanishi, Kenji Tateishi, Toshikazu Fukushima
2002	J	`jnl`	Text classification using ESC-based stochastic decision lists. Inf. Process. Manag. Hang Li, Kenji Yamanishi
2001	A*	`conf`	Discovering outlier filtering rules from unlabeled data: combining a supervised learner with an unsupervised learner. KDD Kenji Yamanishi, Jun'ichi Takeuchi
2001	A*	`conf`	Mining from open answers in questionnaire data. KDD Hang Li, Kenji Yamanishi
2000	A*	`conf`	On-line unsupervised outlier detection using finite mixtures with discounting learning algorithms. KDD Kenji Yamanishi, Jun'ichi Takeuchi, Graham J. Williams, Peter Milne
2000	A*	`conf`	Topic Analysis Using a Finite Mixture Model. EMNLP Hang Li, Kenji Yamanishi
1999	J	`jnl`	Distributed Cooperative Bayesian Learning Strategies. Inf. Comput. Kenji Yamanishi
1999	B	`conf`	Extended Stochastic Complexity and Minimax Relative Loss Analysis. ALT Kenji Yamanishi
1999	A	`conf`	Text Classification Using ESC-based Stochastic Decision Lists. CIKM Hang Li, Kenji Yamanishi
1998	J	`jnl`	A Decision-Theoretic Extension of Stochastic Complexity and Its Applications to Learning. IEEE Trans. Inf. Theory Kenji Yamanishi
1998	A*	`conf`	Minimax Relative Loss Analysis for Sequential Prediction Algorithms Using Parametric Hypotheses. COLT Kenji Yamanishi
1997	A*	`conf`	Distributed Cooperative Bayesian Learning Strategies. COLT Kenji Yamanishi
1997	J	`jnl`	Document Classification Using a Finite Mixture Model CoRR Hang Li, Kenji Yamanishi
1997	A*	`conf`	Document Classification Using a Finite Mixture Model. ACL Hang Li, Kenji Yamanishi
1997	J	`jnl`	On-Line Maximum Likelihood Prediction with Respect to General Loss Functions. J. Comput. Syst. Sci. Kenji Yamanishi
1996	A*	`conf`	A Randomized Approximation of the MDL for Stochastic Models with Hidden Variables. COLT Kenji Yamanishi
1995	J	`jnl`	A Loss Bound Model for On-Line Stochastic Prediction Algorithms Inf. Comput. Kenji Yamanishi
1995	—	`conf`	On-line maximum likelihood prediction with respect to general loss functions. EuroCOLT Kenji Yamanishi
1995	J	`jnl`	Probably Almost Discriminative Learning. Mach. Learn. Kenji Yamanishi
1995	A*	`conf`	Randomized Approximate Aggregating Strategies and Their Applications to Prediction and Discrimination. COLT Kenji Yamanishi
1995	J	`jnl`	alpha-Helix region prediction with stochastic rule learning. Comput. Appl. Biosci. Hiroshi Mamitsuka, Kenji Yamanishi
1994	A*	`conf`	The Minimum COLT Kenji Yamanishi
1993	—	`conf`	Learning non-parametric smooth rules by stochastic rules with finite partitioning. EuroCOLT Kenji Yamanishi
1993	A*	`conf`	On Polynomial-Time Probably almost Discriminative Learnability. COLT Kenji Yamanishi
1992	J	`jnl`	A Learning Criterion for Stochastic Rules. Mach. Learn. Kenji Yamanishi
1992	A*	`conf`	Probably Almost Discriminative Learning. COLT Kenji Yamanishi
1992	B	`conf`	Protein Secondary Structure Prediction Based on Stochastic-Rule Learning. ALT Hiroshi Mamitsuka, Kenji Yamanishi
1991	A*	`conf`	A Loss Bound Model for On-Line Stochastic Prediction Strategies. COLT Kenji Yamanishi
1991	—	`conf`	Learning Stochastic Motifs from Genetic Sequences. ML Kenji Yamanishi, Akihiko Konagaya
1991	B	`conf`	Learning non-parametric densities by finite-dimensional parametric hypotheses. ALT Kenji Yamanishi
1990	A*	`conf`	A Learning Criterion for Stochastic Rules. COLT Kenji Yamanishi