K. Yamanishi

发表

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

Kenji Yamanishi, K. Yamanishi, 1998, IEEE Trans. Inf. Theory.

A learning criterion for stochastic rules

Kenji Yamanishi, K. Yamanishi, 1990, COLT '90.

Dynamic syslog mining for network failure monitoring

Kenji Yamanishi, Yuko Maruyama, K. Yamanishi, 2005, KDD '05.

Mining product reputations on the Web

Satoshi Morinaga, Kenji Yamanishi, Toshikazu Fukushima, 2002, KDD.

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

Kenji Yamanishi, K. Yamanishi, 1997, J. Comput. Syst. Sci..

A Loss Bound Model for On-Line Stochastic Prediction Algorithms

Kenji Yamanishi, K. Yamanishi, 1995, Inf. Comput..

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

Kenji Yamanishi, Jun'ichi Takeuchi, J. Takeuchi, 2002, KDD.

Text classification using ESC-based stochastic decision lists

Hang Li, Kenji Yamanishi, K. Yamanishi, 1999, CIKM '99.

Early detection of persistent topics in social networks

Ryota Tomioka, Kenji Yamanishi, Shota Saito, 2014, 2014 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM 2014).

Long-tailed distributions of inter-event times as mixtures of exponential distributions

Kenji Yamanishi, Naoki Masuda, K. Yamanishi, 2019, Royal Society Open Science.

CHANGE DETECTION , HYPOTHESIS TESTING , AND DATA COMPRESSION

K. Yamanishi, Eiichi Sakurai, Hiroki Kanazawa, 2012 .

Protein α-Helix Region Prediction Using Stochastic-Rule Learning

K. Yamanishi, Hiroshi Mamitsuka, 1992 .

Kernel Complexity for Nonparametric Distributions and Detection of Its Changes

K. Yamanishi, So Hirai, 2019 .

Probably Almost Discriminative Learning

Kenji Yamanishi, K. Yamanishi, 2004, Machine Learning.

Discovering Emerging Topics in Social Streams via Link-Anomaly Detection

Ryota Tomioka, K. Yamanishi, Toshimitsu Takahashi, 2011, IEEE Transactions on Knowledge and Data Engineering.

A basket two-part model to analyze medical expenditure on interdependent multiple sectors

K. Yamanishi, S. Sugawara, Tianyi Wu, 2018, Statistical methods in medical research.

Title Predicting disease progression from short biomarker seriesusing expert advice algorithm

H. Kashima, K. Yamanishi, K. Aihara, 2017 .

Predicting 10-2 Visual Field From Optical Coherence Tomography in Glaucoma Using Deep Learning Corrected With 24-2/30-2 Visual Field

K. Yamanishi, Taichi Kiwaki, Hiroshi Murata, 2021, Translational vision science & technology.

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

Philip S. Yu, Jing Wang, Jiannong Cao, 2021, IEEE Transactions on Knowledge and Data Engineering.

Improving visual field trend analysis with optical coherence tomography and deeply-regularized latent-space linear regression.

K. Yamanishi, Taichi Kiwaki, Hiroshi Murata, 2020, Ophthalmology. Glaucoma.

On-line unsupervised outlier detection using finite mixtures with discounting learning algorithms

Graham J. Williams, J. Takeuchi, K. Yamanishi, 2000, KDD.

Predicting the glaucomatous central 10 degrees visual field from optical coherence tomography using deep learning and tensor regression.

K. Yamanishi, Taichi Kiwaki, Hiroshi Murata, 2020, American journal of ophthalmology.

Deep learning model to predict visual field in central 10° from optical coherence tomography measurement in glaucoma

K. Yamanishi, Taichi Kiwaki, Hiroshi Murata, 2020, British Journal of Ophthalmology.

Generalization Error Bound for Hyperbolic Ordinal Embedding pdf

Marc Cavazza, Kenji Yamanishi, Atsushi Nitanda, 2021, ICML.

Discovery of Deep Knowledge from Complex Data

Kenji Yamanishi, K. Yamanishi, 2015 .

Construction and performance evaluation of fermat codes

K. Yamanishi, 1990 .

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

K. Yamanishi, So Hirai, 2021, 2021 IEEE International Conference on Big Data (Big Data).

Topic Analysis Using a Finite Mixture Model

K. Yamanishi, Hang Li, 2000, EMNLP.

Document Classification Using a Finite Mixture Model

Hang Li, Kenji Yamanishi, K. Yamanishi, 1997, ACL.

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

K. Yamanishi, M. Okada, S. Sugawara, 2019, Data Mining and Knowledge Discovery.

Predicting disease progression from short biomarker series using expert advice algorithm

Kazuyuki Aihara, Hisashi Kashima, Kai Morino, 2015, Scientific Reports.

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

Kenji Yamanishi, Jun'ichi Takeuchi, J. Takeuchi, 2001, KDD '01.

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

Tomoharu Iwata, Kenji Yamanishi, Tomohiko Nakmaura, 2017, 2017 IEEE International Conference on Data Science and Advanced Analytics (DSAA).

Change sign detection with differential MDL change statistics and its applications to COVID-19 pandemic analysis pdf

Kenji Yamanishi, Linchuan Xu, Ryo Yuki, 2020, Scientific Reports.

A Learning Criterion for Stochastic Rules

Kenji Yamanishi, K. Yamanishi, 2004, Machine Learning.

Topic Analysis Using a Finite Mixture Model

Hang Li, Kenji Yamanishi, K. Yamanishi, 2000, Inf. Process. Manag..

Summarizing Finite Mixture Model with Overlapping Quantification

Kenji Yamanishi, Shunki Kyoya, K. Yamanishi, 2021, Entropy.

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

Siamak Yousefi, Kenji Yamanishi, Hiroshi Murata, 2018, KDD.

Detection of Longitudinal Visual Field Progression in Glaucoma Using Machine Learning.

Siamak Yousefi, Kenji Yamanishi, Taichi Kiwaki, 2018, American journal of ophthalmology.

alpha-Helix region prediction with stochastic rule learning

Kenji Yamanishi, Hiroshi Mamitsuka, K. Yamanishi, 1995, Comput. Appl. Biosci..

Protein alpha -helix region prediction based on stochastic-rule learning

Kenji Yamanishi, Hiroshi Mamitsuka, K. Yamanishi, 1993, [1993] Proceedings of the Twenty-sixth Hawaii International Conference on System Sciences.

Protein Secondary Structure Prediction Based on Stochastic-Rule Learning

Kenji Yamanishi, Hiroshi Mamitsuka, K. Yamanishi, 1992, ALT.

Document Classification Using a Finite Mixture Model

Hang Li, Kenji Yamanishi, K. Yamanishi, 1997, ACL.

A unifying framework for detecting outliers and change points from time series

Kenji Yamanishi, Jun'ichi Takeuchi, J. Takeuchi, 2006, IEEE Transactions on Knowledge and Data Engineering.

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

Graham J. Williams, Peter Milne, Kenji Yamanishi, 2000, KDD '00.

Hyperbolic Ordinal Embedding

Feng Tian, Atsushi Suzuki, Kenji Yamanishi, 2019, ACML.

Fourth Workshop on Information Theoretic Methods in Science and Engineering

Ioan Tabus, Jorma Rissanen, Kenji Yamanishi, 2011 .

Data mining for knowledge organization

Satoshi Morinaga, Kenji Yamanishi, K. Yamanishi, 2005 .

Network anomaly detection based on Eigen equation compression

Kenji Yamanishi, Takayuki Nakata, Ryohei Fujimaki, 2009, KDD.

A randomized approximation of the MDL for stochastic models with hidden variables

Kenji Yamanishi, K. Yamanishi, 1996, COLT '96.

The minimum L-complexity algorithm and its applications to learning non-parametric rules

Kenji Yamanishi, K. Yamanishi, 1994, COLT '94.

Learning Stochastic Motifs from Genetic Sequences

Akihiko Konagaya, Kenji Yamanishi, K. Yamanishi, 1991, ML.

Mixture Complexity and Its Application to Gradual Clustering Change Detection pdf

Kenji Yamanishi, Shunki Kyoya, K. Yamanishi, 2020, Entropy.

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

Kenji Yamanishi, Takayuki Nakata, Akinori Sato, 2009, Stat. Anal. Data Min..

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

Kenji Yamanishi, Shintaro Fukushima, K. Yamanishi, 2019, Entropy.

Model Change Detection With the MDL Principle

Kenji Yamanishi, Shintaro Fukushima, K. Yamanishi, 2018, IEEE Transactions on Information Theory.

Online detection of continuous changes in stochastic processes

K. Yamanishi, Kohei Miyaguchi, 2017, International Journal of Data Science and Analytics.

Detecting gradual changes from data stream using MDL-change statistics

Kenji Yamanishi, Kohei Miyaguchi, K. Yamanishi, 2016, 2016 IEEE International Conference on Big Data (Big Data).

Online detection of continuous changes in stochastic processes

Kenji Yamanishi, Kohei Miyaguchi, K. Yamanishi, 2015, International Journal of Data Science and Analytics.

Dynamic Model Selection With its Applications to Novelty Detection

Kenji Yamanishi, Yuko Maruyama, K. Yamanishi, 2007, IEEE Transactions on Information Theory.

Dynamic model selection with its applications to computer security

Kenji Yamanishi, Yuko Maruyama, K. Yamanishi, 2004, Information Theory Workshop.

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

Kenji Yamanishi, K. Yamanishi, 1995, J. Comput. Syst. Sci..

Tracking dynamics of topic trends using a finite mixture model

Satoshi Morinaga, Kenji Yamanishi, K. Yamanishi, 2004, KDD.

Traffic Risk Mining From Heterogeneous Road Statistics

Shin Matsushima, Kenji Yamanishi, Koichi Moriya, 2018, IEEE Transactions on Intelligent Transportation Systems.

Traffic Risk Mining Using Partially Ordered Non-Negative Matrix Factorization

Shin Matsushima, Kenji Yamanishi, Taito Lee, 2016, 2016 IEEE International Conference on Data Science and Advanced Analytics (DSAA).

Traffic risk mining from heterogeneous road statistics

Shin Matsushima, Kenji Yamanishi, Koichi Moriya, 2015, 2015 IEEE International Conference on Data Science and Advanced Analytics (DSAA).

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

Kenji Yamanishi, Yu Hayashi, K. Yamanishi, 2012, 2012 IEEE 12th International Conference on Data Mining.

Detecting Signs of Model Change with Continuous Model Selection Based on Descriptive Dimensionality

K. Yamanishi, So Hirai, 2023, Applied Intelligence.

Detecting Hierarchical Changes in Latent Variable Models pdf

Kenji Yamanishi, Shintaro Fukushima, K. Yamanishi, 2020, 2020 IEEE International Conference on Data Mining (ICDM).

Online Robust and Adaptive Learning from Data Streams

K. Yamanishi, Atsushi Nitanda, Shintaro Fukushima, 2020, ArXiv.

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

Kenji Yamanishi, So Hirai, K. Yamanishi, 2019, 2019 IEEE International Conference on Big Data (Big Data).

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

Kenji Yamanishi, K. Yamanishi, 2019, ArXiv.

Multi-label learning with missing and completely unobserved labels

Kenji Yamanishi, Kun Qian, Jing Wang, 2021, Data Min. Knowl. Discov..

Grafting for combinatorial binary model using frequent itemset mining

Kenji Yamanishi, Shin Matsushima, Taito Lee, 2020, Data Mining and Knowledge Discovery.

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

Taiji Suzuki, K. Yamanishi, Atsushi Nitanda, 2023, ArXiv.

A Joint Multitask Learning Model for Cross-sectional and Longitudinal Predictions of Visual Field Using OCT

Kenji Yamanishi, Taichi Kiwaki, Kazuhiko Mori, 2021, Ophthalmology science.

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

Kenji Yamanishi, Taichi Kiwaki, Ryo Asaoka, 2021, KDD.

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

Kenji Yamanishi, Eiichi Sakurai, K. Yamanishi, 2012, 2012 IEEE Information Theory Workshop.

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

K. Yamanishi, M. Cavazza, Atsushi Nitanda, 2021, NeurIPS.

Grafting for combinatorial binary model using frequent itemset mining

K. Yamanishi, Shin Matsushima, Taito Lee, 2019, Data Mining and Knowledge Discovery.

Grafting for combinatorial binary model using frequent itemset mining pdf

Shin Matsushima, Kenji Yamanishi, Taito Lee, 2017, Data Mining and Knowledge Discovery.

Mining from open answers in questionnaire data

K. Yamanishi, Hang Li, 2001, KDD '01.

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

K. Yamanishi, Yuichi Ike, Naoki Nishikawa, 2023, NeurIPS.

Proceedings of the Fifth Workshop on Information Theoretic Methods in Science and Engineering

S. deRooij, Kenji Yamanishi, Teemu Roos, 2012 .

Ranking Preserving Nonnegative Matrix Factorization

Weiwei Liu, Jing Wang, Xiao Wang, 2018, IJCAI.

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

Kenji Yamanishi, Tianyi Wu, Shinya Sugawara, 2019, Data Mining and Knowledge Discovery.

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

Kenji Yamanishi, So Hirai, K. Yamanishi, 2011, 2011 IEEE International Symposium on Information Theory Proceedings.

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

Kenji Yamanishi, Pham Thuc Hung, K. Yamanishi, 2020, Entropy.

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

Kenji Yamanishi, Kohei Miyaguchi, K. Yamanishi, 2018, AISTATS.

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

Kai Morino, Kenji Yamanishi, Hiroshi Murata, 2016, ArXiv.

Minimax relative loss analysis for sequential prediction algorithms using parametric hypotheses

Kenji Yamanishi, K. Yamanishi, 1998, COLT' 98.

Predicting glaucoma progression using multi-task learning with heterogeneous features

Kai Morino, Kenji Yamanishi, Shigeru Maya, 2014, 2014 IEEE International Conference on Big Data (Big Data).

Network Change Detection Based on Random Walk in Latent Space

K. Yamanishi, Linchuan Xu, Chuan-hao Lin, 2023, IEEE Transactions on Knowledge and Data Engineering.

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

Jun Huang, Kenji Yamanishi, Atsushi Nitanda, 2020, ArXiv.

Distributed cooperative Bayesian learning strategies

Kenji Yamanishi, K. Yamanishi, 1997, COLT '97.

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

Kenji Yamanishi, Tianyi Wu, Makoto Okada, 2019, Data Mining and Knowledge Discovery.

Graph Summarization with Latent Variable Probabilistic Models

K. Yamanishi, Shintaro Fukushima, Ryoga Kanai, 2021, COMPLEX NETWORKS.

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

Kenji Yamanishi, So Hirai, K. Yamanishi, 2013, IEEE Transactions on Information Theory.

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

Ryota Tomioka, Kenji Yamanishi, Hiroki Iwai, 2011, PAKDD.

Glaucoma Progression Prediction Using Retinal Thickness via Latent Space Linear Regression

Kenji Yamanishi, Hiroshi Murata, Linchuan Xu, 2019, KDD.

Predicting Glaucomatous Progression with Piecewise Regression Model from Heterogeneous Medical Data

Kai Morino, Kenji Yamanishi, Hiroshi Murata, 2016, HEALTHINF.

Topic Analysis Using a Finite Mixture Model

K. Yamanishi, Hang Li, 2000, Inf. Process. Manag..

Latent variable mining with its applications to anomalous behavior detection

Kenji Yamanishi, Shunsuke Hirose, K. Yamanishi, 2009, Statistical analysis and data mining.