Kenji Yamanishi

According to our database, Kenji Yamanishi authored at least 125 papers between 1991 and 2024.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of four.

2024

GMMDA: Gaussian mixture modeling of graph in latent space for graph data augmentation.

Yanjin Li, Linchuan Xu, Kenji Yamanishi

Knowl. Inf. Syst., December, 2024

Foundation of Calculating Normalized Maximum Likelihood for Continuous Probability Models.

Atsushi Suzuki, Kota Fukuzawa, Kenji Yamanishi

CoRR, 2024

Clustering Change Sign Detection by Fusing Mixture Complexity.

Kento Urano, Ryo Yuki, Kenji Yamanishi

CoRR, 2024

Graph Community Augmentation with GMM-Based Modeling in Latent Space.

Shintaro Fukushima, Kenji Yamanishi

Proceedings of the IEEE International Conference on Data Mining, 2024

Luckiness Normalized Maximum Likelihood-based Change Detection for High-dimensional Graphical Models with Missing Data.

Zhongyi Que, Linchuan Xu, Kenji Yamanishi

Proceedings of the IEEE International Conference on Big Data, 2024

2023

Dimensionality selection for hyperbolic embeddings using decomposed normalized maximum likelihood code-length.

Ryo Yuki, Yuichi Ike, Kenji Yamanishi

Knowl. Inf. Syst., December, 2023

Detecting signs of model change with continuous model selection based on descriptive dimensionality.

Kenji Yamanishi, So Hirai

Appl. Intell., November, 2023

Network Change Detection Based on Random Walk in Latent Space.

Chuan-hao Lin, Linchuan Xu, Kenji Yamanishi

IEEE Trans. Knowl. Data Eng., June, 2023

Adaptive Topological Feature via Persistent Homology: Filtration Learning for Point Clouds.

Naoki Nishikawa, Yuichi Ike, Kenji Yamanishi

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

Tight and fast generalization error bound of graph embedding in metric space.

Atsushi Suzuki, Atsushi Nitanda, Taiji Suzuki, Jing Wang, Feng Tian, Kenji Yamanishi

Proceedings of the International Conference on Machine Learning, 2023

Dimensionality and Curvature Selection of Graph Embedding using Decomposed Normalized Maximum Likelihood Code-Length.

Ryo Yuki, Atsushi Suzuki, Kenji Yamanishi

Proceedings of the IEEE International Conference on Data Mining, 2023

Balancing Summarization and Change Detection in Graph Streams.

Shintaro Fukushima, Kenji Yamanishi

Proceedings of the IEEE International Conference on Data Mining, 2023

Learning with the Minimum Description Length Principle

Kenji Yamanishi

Springer, ISBN: 978-981-99-1789-1, 2023

2022

Mixture Complexity and Its Application to Gradual Clustering Change Detection.

Shunki Kyoya, Kenji Yamanishi

Entropy, 2022

Dimensionality Selection of Hyperbolic Graph Embeddings using Decomposed Normalized Maximum Likelihood Code-Length.

Ryo Yuki, Yuichi Ike, Kenji Yamanishi

Proceedings of the IEEE International Conference on Data Mining, 2022

Change Detection with Probabilistic Models on Persistence Diagrams.

Kohei Ueda, Yuichi Ike, Kenji Yamanishi

Proceedings of the IEEE International Conference on Data Mining, 2022

2021

MixSp: A Framework for Embedding Heterogeneous Information Networks With Arbitrary Number of Node and Edge Types.

Linchuan Xu, Jing Wang, Lifang He, Jiannong Cao, Xiaokai Wei, Philip S. Yu, Kenji Yamanishi

IEEE Trans. Knowl. Data Eng., 2021

Fourier-Analysis-Based Form of Normalized Maximum Likelihood: Exact Formula and Relation to Complex Bayesian Prior.

Atsushi Suzuki, Kenji Yamanishi

IEEE Trans. Inf. Theory, 2021

Summarizing Finite Mixture Model with Overlapping Quantification.

Shunki Kyoya, Kenji Yamanishi

Entropy, 2021

Word2vec Skip-Gram Dimensionality Selection via Sequential Normalized Maximum Likelihood.

Pham Thuc Hung, Kenji Yamanishi

Entropy, 2021

Multi-label learning with missing and completely unobserved labels.

Jun Huang, Linchuan Xu, Kun Qian, Jing Wang, Kenji Yamanishi

Data Min. Knowl. Discov., 2021

Generalization Bounds for Graph Embedding Using Negative Sampling: Linear vs Hyperbolic.

Atsushi Suzuki, Atsushi Nitanda, Jing Wang, Linchuan Xu, Kenji Yamanishi, Marc Cavazza

Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

PAMI: A Computational Module for Joint Estimation and Progression Prediction of Glaucoma.

Linchuan Xu, Ryo Asaoka, Taichi Kiwaki, Hiroshi Murata, Yuri Fujino, Kenji Yamanishi

Proceedings of the KDD '21: The 27th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, 2021

Generalization Error Bound for Hyperbolic Ordinal Embedding.

Atsushi Suzuki, Atsushi Nitanda, Jing Wang, Linchuan Xu, Kenji Yamanishi, Marc Cavazza

Proceedings of the 38th International Conference on Machine Learning, 2021

Graph Summarization with Latent Variable Probabilistic Models.

Shintaro Fukushima, Ryoga Kanai, Kenji Yamanishi

Proceedings of the Complex Networks & Their Applications X - Volume 2, Proceedings of the Tenth International Conference on Complex Networks and Their Applications COMPLEX NETWORKS 2021, Madrid, Spain, November 30, 2021

Detecting Gradual Structure Changes of Non-parametric Distributions via Kernel Complexity.

So Hirai, Kenji Yamanishi

Proceedings of the 2021 IEEE International Conference on Big Data (Big Data), 2021

2020

Grafting for combinatorial binary model using frequent itemset mining.

Taito Lee, Shin Matsushima, Kenji Yamanishi

Data Min. Knowl. Discov., 2020

A Novel Global Spatial Attention Mechanism in Convolutional Neural Network for Medical Image Classification.

Linchuan Xu, Jun Huang, Atsushi Nitanda, Ryo Asaoka, Kenji Yamanishi

CoRR, 2020

Change Sign Detection with Differential MDL Change Statistics and its Applications to COVID-19 Pandemic Analysis.

Kenji Yamanishi, Linchuan Xu, Ryo Yuki, Shintaro Fukushima, Chuan-hao Lin

CoRR, 2020

Online Robust and Adaptive Learning from Data Streams.

Shintaro Fukushima, Atsushi Nitanda, Kenji Yamanishi

CoRR, 2020

Discovering Latent Class Labels for Multi-Label Learning.

Jun Huang, Linchuan Xu, Jing Wang, Lei Feng, Kenji Yamanishi

Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence, 2020

Detecting Hierarchical Changes in Latent Variable Models.

Shintaro Fukushima, Kenji Yamanishi

Proceedings of the 20th IEEE International Conference on Data Mining, 2020

2019

Correction to Efficient Computation of Normalized Maximum Likelihood Codes for Gaussian Mixture Models With Its Applications to Clustering.

So Hirai, Kenji Yamanishi

IEEE Trans. Inf. Theory, 2019

Detecting Metachanges in Data Streams from the Viewpoint of the MDL Principle.

Shintaro Fukushima, Kenji Yamanishi

Entropy, 2019

Model Selection for Non-Negative Tensor Factorization with Minimum Description Length.

Yunhui Fu, Shin Matsushima, Kenji Yamanishi

Entropy, 2019

The decomposed normalized maximum likelihood code-length criterion for selecting hierarchical latent variable models.

Kenji Yamanishi, Tianyi Wu, Shinya Sugawara, Makoto Okada

Data Min. Knowl. Discov., 2019

Descriptive Dimensionality and Its Characterization of MDL-based Learning and Change Detection.

Kenji Yamanishi

CoRR, 2019

Glaucoma Progression Prediction Using Retinal Thickness via Latent Space Linear Regression.

Yuhui Zheng, Linchuan Xu, Taichi Kiwaki, Jing Wang, Hiroshi Murata, Ryo Asaoka, Kenji Yamanishi

Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, 2019

Modern MDL meets Data Mining Insights, Theory, and Practice.

Jilles Vreeken, Kenji Yamanishi

Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, 2019

Attributed Subspace Clustering.

Jing Wang, Linchuan Xu, Feng Tian, Atsushi Suzuki, Changqing Zhang, Kenji Yamanishi

Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence, 2019

Detecting Model Changes and their Early Warning Signals Using MDL Change Statistics.

So Hirai, Kenji Yamanishi

Proceedings of the 2019 IEEE International Conference on Big Data (IEEE BigData), 2019

Adaptive Minimax Regret against Smooth Logarithmic Losses over High-Dimensional l1-Balls via Envelope Complexity.

Kohei Miyaguchi, Kenji Yamanishi

Proceedings of the 22nd International Conference on Artificial Intelligence and Statistics, 2019

Hyperbolic Ordinal Embedding.

Atsushi Suzuki, Jing Wang, Feng Tian, Atsushi Nitanda, Kenji Yamanishi

Proceedings of The 11th Asian Conference on Machine Learning, 2019

Orderly Subspace Clustering.

Jing Wang, Atsushi Suzuki, Linchuan Xu, Feng Tian, Liang Yang, Kenji Yamanishi

Proceedings of the Thirty-Third AAAI Conference on Artificial Intelligence, 2019

2018

Traffic Risk Mining From Heterogeneous Road Statistics.

Koichi Moriya, Shin Matsushima, Kenji Yamanishi

IEEE Trans. Intell. Transp. Syst., 2018

Model Change Detection With the MDL Principle.

Kenji Yamanishi, Shintaro Fukushima

IEEE Trans. Inf. Theory, 2018

High-dimensional penalty selection via minimum description length principle.

Kohei Miyaguchi, Kenji Yamanishi

Mach. Learn., 2018

Adaptive Minimax Regret against Smooth Logarithmic Losses over High-Dimensional ε₁-Balls via Envelope Complexity.

Kohei Miyaguchi, Kenji Yamanishi

CoRR, 2018

Stable Geodesic Update on Hyperbolic Space and its Application to Poincare Embeddings.

Yosuke Enokida, Atsushi Suzuki, Kenji Yamanishi

CoRR, 2018

Estimating Glaucomatous Visual Sensitivity from Retinal Thickness with Pattern-Based Regularization and Visualization.

Hiroki Sugiura, Taichi Kiwaki, Siamak Yousefi, Hiroshi Murata, Ryo Asaoka, Kenji Yamanishi

Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, 2018

Exact Calculation of Normalized Maximum Likelihood Code Length Using Fourier Analysis.

Atsushi Suzuki, Kenji Yamanishi

Proceedings of the 2018 IEEE International Symposium on Information Theory, 2018

Ranking Preserving Nonnegative Matrix Factorization.

Jing Wang, Feng Tian, Weiwei Liu, Xiao Wang, Wenjie Zhang, Kenji Yamanishi

Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence, 2018

Detecting Latent Structure Uncertainty with Structural Entropy.

So Hirai, Kenji Yamanishi

Proceedings of the IEEE International Conference on Big Data (IEEE BigData 2018), 2018

2017

Online detection of continuous changes in stochastic processes.

Kohei Miyaguchi, Kenji Yamanishi

Int. J. Data Sci. Anal., 2017

Grafting for Combinatorial Boolean Model using Frequent Itemset Mining.

Taito Lee, Shin Matsushima, Kenji Yamanishi

CoRR, 2017

An Upper Bound on Normalized Maximum Likelihood Codes for Gaussian Mixture Models.

So Hirai, Kenji Yamanishi

CoRR, 2017

Sparse Graphical Modeling via Stochastic Complexity.

Kohei Miyaguchi, Shin Matsushima, Kenji Yamanishi

Proceedings of the 2017 SIAM International Conference on Data Mining, 2017

Decomposed Normalized Maximum Likelihood Codelength Criterion for Selecting Hierarchical Latent Variable Models.

Tianyi Wu, Shinya Sugawara, Kenji Yamanishi

Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Halifax, NS, Canada, August 13, 2017

Multi-view Learning over Retinal Thickness and Visual Sensitivity on Glaucomatous Eyes.

Toshimitsu Uesaka, Kai Morino, Hiroki Sugiura, Taichi Kiwaki, Hiroshi Murata, Ryo Asaoka, Kenji Yamanishi

Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Halifax, NS, Canada, August 13, 2017

Latent Dimensionality Estimation for Probabilistic Canonical Correlation Analysis Using Normalized Maximum Likelihood Code-Length.

Tomohiko Nakmaura, Tomoharu Iwata, Kenji Yamanishi

Proceedings of the 2017 IEEE International Conference on Data Science and Advanced Analytics, 2017

Discovering potential traffic risks in Japan using a supervised learning approach.

Tatsuru Kobayashi, Shin Matsushima, Taito Lee, Kenji Yamanishi

Proceedings of the 2017 IEEE International Conference on Big Data (IEEE BigData 2017), 2017

Detecting changes in streaming data with information-theoretic windowing.

Ryoya Kaneko, Kohei Miyaguchi, Kenji Yamanishi

Proceedings of the 2017 IEEE International Conference on Big Data (IEEE BigData 2017), 2017

2016

Predicting Glaucoma Visual Field Loss by Hierarchically Aggregating Clustering-based Predictors.

Motohide Higaki, Kai Morino, Hiroshi Murata, Ryo Asaoka, Kenji Yamanishi

CoRR, 2016

Rank Selection for Non-negative Matrix Factorization with Normalized Maximum Likelihood Coding.

Yu Ito, Shinichi Oeda, Kenji Yamanishi

Proceedings of the 2016 SIAM International Conference on Data Mining, 2016

Structure Selection for Convolutive Non-negative Matrix Factorization Using Normalized Maximum Likelihood Coding.

Atsushi Suzuki, Kohei Miyaguchi, Kenji Yamanishi

Proceedings of the IEEE 16th International Conference on Data Mining, 2016

Temporal Network Change Detection Using Network Centralities.

Yoshitaro Yonamoto, Kai Morino, Kenji Yamanishi

Proceedings of the 2016 IEEE International Conference on Data Science and Advanced Analytics, 2016

Traffic Risk Mining Using Partially Ordered Non-Negative Matrix Factorization.

Taito Lee, Shin Matsushima, Kenji Yamanishi

Proceedings of the 2016 IEEE International Conference on Data Science and Advanced Analytics, 2016

Web Behavior Analysis Using Sparse Non-Negative Matrix Factorization.

Akihiro Demachi, Shin Matsushima, Kenji Yamanishi

Proceedings of the 2016 IEEE International Conference on Data Science and Advanced Analytics, 2016

Predicting Glaucomatous Progression with Piecewise Regression Model from Heterogeneous Medical Data.

Kyosuke Tomoda, Kai Morino, Hiroshi Murata, Ryo Asaoka, Kenji Yamanishi

Proceedings of the 9th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2016), 2016

Detecting gradual changes from data stream using MDL-change statistics.

Kenji Yamanishi, Kohei Miyaguchi

Proceedings of the 2016 IEEE International Conference on Big Data (IEEE BigData 2016), 2016

2015

Early detection of persistent topics in social networks.

Shota Saito, Ryota Tomioka, Kenji Yamanishi

Soc. Netw. Anal. Min., 2015

Sequential network change detection with its applications to ad impact relation analysis.

Yu Hayashi, Kenji Yamanishi

Data Min. Knowl. Discov., 2015

Discovery of Glaucoma Progressive Patterns Using Hierarchical MDL-Based Clustering.

Shigeru Maya, Kai Morino, Hiroshi Murata, Ryo Asaoka, Kenji Yamanishi

Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2015

On-line detection of continuous changes in stochastic processes.

Kohei Miyaguchi, Kenji Yamanishi

Proceedings of the 2015 IEEE International Conference on Data Science and Advanced Analytics, 2015

2014

Discovering Emerging Topics in Social Streams via Link-Anomaly Detection.

Toshimitsu Takahashi, Ryota Tomioka, Kenji Yamanishi

IEEE Trans. Knowl. Data Eng., 2014

Data Fusion Using Restricted Boltzmann Machines.

Yoshiki Sakai, Kenji Yamanishi

Proceedings of the 2014 IEEE International Conference on Data Mining, 2014

Extracting Latent Skills from Time Series of Asynchronous and Incomplete Examinations.

Shinichi Oeda, Yu Ito, Kenji Yamanishi

Proceedings of the 7th International Conference on Educational Data Mining, 2014

Predicting glaucoma progression using multi-task learning with heterogeneous features.

Shigeru Maya, Kai Morino, Kenji Yamanishi

Proceedings of the 2014 IEEE International Conference on Big Data (IEEE BigData 2014), 2014

2013

Efficient Computation of Normalized Maximum Likelihood Codes for Gaussian Mixture Models With Its Applications to Clustering.

So Hirai, Kenji Yamanishi

IEEE Trans. Inf. Theory, 2013

Graph Partitioning Change Detection Using Tree-Based Clustering.

Sho-Ichi Sato, Kenji Yamanishi

Proceedings of the 2013 IEEE 13th International Conference on Data Mining, 2013

Quantitative Prediction of Glaucomatous Visual Field Loss from Few Measurements.

Zenghan Liang, Ryota Tomioka, Hiroshi Murata, Ryo Asaoka, Kenji Yamanishi

Proceedings of the 2013 IEEE 13th International Conference on Data Mining, 2013

Extracting Time-evolving Latent Skills from Examination Time Series.

Shinichi Oeda, Kenji Yamanishi

Proceedings of the 6th International Conference on Educational Data Mining, 2013

An NML-based model selection criterion for general relational data modeling.

Yoshiki Sakai, Kenji Yamanishi

Proceedings of the 2013 IEEE International Conference on Big Data (IEEE BigData 2013), 2013

2012

Normalized Maximum Likelihood Coding for Exponential Family with Its Applications to Optimal Clustering

So Hirai, Kenji Yamanishi

CoRR, 2012

Detecting changes of clustering structures using normalized maximum likelihood coding.

So Hirai, Kenji Yamanishi

Proceedings of the 18th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2012

Comparison of dynamic model selection with infinite HMM for statistical model change detection.

Eiichi Sakurai, Kenji Yamanishi

Proceedings of the 2012 IEEE Information Theory Workshop, 2012

An MDL-based change-detection algorithm with its applications to learning piecewise stationary memoryless sources.

Hiroki Kanazawa, Kenji Yamanishi

Proceedings of the 2012 IEEE Information Theory Workshop, 2012

2011

Real-Time Change-Point Detection Using Sequentially Discounting Normalized Maximum Likelihood Coding.

Yasuhiro Urabe, Kenji Yamanishi, Ryota Tomioka, Hiroki Iwai

Proceedings of the Advances in Knowledge Discovery and Data Mining, 2011

Efficient computation of normalized maximum likelihood coding for Gaussian mixtures with its applications to optimal clustering.

So Hirai, Kenji Yamanishi

Proceedings of the 2011 IEEE International Symposium on Information Theory Proceedings, 2011

2009

Latent variable mining with its applications to anomalous behavior detection.

Shunsuke Hirose, Kenji Yamanishi

Stat. Anal. Data Min., 2009

Mining abnormal patterns from heterogeneous time-series with irrelevant features for fault event detection.

Ryohei Fujimaki, Takayuki Nakata, Hidenori Tsukahara, Akinori Sato, Kenji Yamanishi

Stat. Anal. Data Min., 2009

Network anomaly detection based on Eigen equation compression.

Shunsuke Hirose, Kenji Yamanishi, Takayuki Nakata, Ryohei Fujimaki

Proceedings of the 15th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Paris, France, June 28, 2009

2007

Dynamic Model Selection With its Applications to Novelty Detection.

Kenji Yamanishi, Yuko Maruyama

IEEE Trans. Inf. Theory, 2007

2006

A Unifying Framework for Detecting Outliers and Change Points from Time Series.

Jun'ichi Takeuchi, Kenji Yamanishi

IEEE Trans. Knowl. Data Eng., 2006

2005

Dynamic syslog mining for network failure monitoring.

Kenji Yamanishi, Yuko Maruyama

Proceedings of the Eleventh ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2005

2004

On-Line Unsupervised Outlier Detection Using Finite Mixtures with Discounting Learning Algorithms.

Kenji Yamanishi, Jun'ichi Takeuchi, Graham J. Williams, Peter Milne

Data Min. Knowl. Discov., 2004

Tracking dynamics of topic trends using a finite mixture model.

Satoshi Morinaga, Kenji Yamanishi

Proceedings of the Tenth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2004

Dynamic model selection with its applications to computer security.

Yuko Maruyama, Kenji Yamanishi

Proceedings of the 2004 IEEE Information Theory Workshop, 2004

2003

Topic analysis using a finite mixture model.

Hang Li, Kenji Yamanishi

Inf. Process. Manag., 2003

Distributed cooperative mining for information consortia.

Satoshi Morinaga, Kenji Yamanishi, Jun'ichi Takeuchi

Proceedings of the Ninth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Washington, DC, USA, August 24, 2003

2002

Text classification using ESC-based stochastic decision lists.

Hang Li, Kenji Yamanishi

Inf. Process. Manag., 2002

Mining Open Answers in Questionnaire Data.

Kenji Yamanishi, Hang Li

IEEE Intell. Syst., 2002

A unifying framework for detecting outliers and change points from non-stationary time series data.

Kenji Yamanishi, Jun'ichi Takeuchi

Proceedings of the Eighth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2002

Mining product reputations on the Web.

Satoshi Morinaga, Kenji Yamanishi, Kenji Tateishi, Toshikazu Fukushima

Proceedings of the Eighth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2002

2001

Discovering outlier filtering rules from unlabeled data: combining a supervised learner with an unsupervised learner.

Kenji Yamanishi, Jun'ichi Takeuchi

Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining, 2001

Mining from open answers in questionnaire data.

Hang Li, Kenji Yamanishi

Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining, 2001

1999

Distributed Cooperative Bayesian Learning Strategies.

Kenji Yamanishi

Inf. Comput., 1999

Extended Stochastic Complexity and Minimax Relative Loss Analysis.

Kenji Yamanishi

Proceedings of the Algorithmic Learning Theory, 10th International Conference, 1999

1998

A Decision-Theoretic Extension of Stochastic Complexity and Its Applications to Learning.

Kenji Yamanishi

IEEE Trans. Inf. Theory, 1998

Minimax Relative Loss Analysis for Sequential Prediction Algorithms Using Parametric Hypotheses.

Kenji Yamanishi

Proceedings of the Eleventh Annual Conference on Computational Learning Theory, 1998

1997

On-Line Maximum Likelihood Prediction with Respect to General Loss Functions.

Kenji Yamanishi

J. Comput. Syst. Sci., 1997

Document Classification Using a Finite Mixture Model.

Hang Li, Kenji Yamanishi

Proceedings of the 35th Annual Meeting of the Association for Computational Linguistics and 8th Conference of the European Chapter of the Association for Computational Linguistics, 1997

1996

A Randomized Approximation of the MDL for Stochastic Models with Hidden Variables.

Kenji Yamanishi

Proceedings of the Ninth Annual Conference on Computational Learning Theory, 1996

1995

A Loss Bound Model for On-Line Stochastic Prediction Algorithms

Kenji Yamanishi

Inf. Comput., May, 1995

Probably Almost Discriminative Learning.

Kenji Yamanishi

Mach. Learn., 1995

alpha-Helix region prediction with stochastic rule learning.

Hiroshi Mamitsuka, Kenji Yamanishi

Comput. Appl. Biosci., 1995

Randomized Approximate Aggregating Strategies and Their Applications to Prediction and Discrimination.

Kenji Yamanishi

Proceedings of the Eigth Annual Conference on Computational Learning Theory, 1995

1994

The Minimum <i>L</i>-Complexity Algorithm and its Applications to Learning Non-Parametric Rules.

Kenji Yamanishi

Proceedings of the Seventh Annual ACM Conference on Computational Learning Theory, 1994

1993

Learning non-parametric smooth rules by stochastic rules with finite partitioning.

Kenji Yamanishi

Proceedings of the First European Conference on Computational Learning Theory, 1993

On Polynomial-Time Probably almost Discriminative Learnability.

Kenji Yamanishi

Proceedings of the Sixth Annual ACM Conference on Computational Learning Theory, 1993

1992

A Learning Criterion for Stochastic Rules.

Kenji Yamanishi

Mach. Learn., 1992

Protein Secondary Structure Prediction Based on Stochastic-Rule Learning.

Hiroshi Mamitsuka, Kenji Yamanishi

Proceedings of the Algorithmic Learning Theory, Third Workshop, 1992

1991

Learning Stochastic Motifs from Genetic Sequences.

Kenji Yamanishi, Akihiko Konagaya

Proceedings of the Eighth International Workshop (ML91), 1991

A Loss Bound Model for On-Line Stochastic Prediction Strategies.

Kenji Yamanishi

Proceedings of the Fourth Annual Workshop on Computational Learning Theory, 1991

Learning non-parametric densities by finite-dimensional parametric hypotheses.

Kenji Yamanishi

Proceedings of the Algorithmic Learning Theory, 2nd International Workshop, 1991