Yoshitaka Kameya

发表

In defense of PDDL axioms

Katsumi Inoue, Taisuke Sato, Masakazu Ishihata, 2003, Artif. Intell..

Statistical Abduction with Tabulation

Taisuke Sato, Yoshitaka Kameya, 2002, Computational Logic: Logic Programming and Beyond.

Variational Bayes via propositionalized probability computation in PRISM

Kenichi Kurihara, Taisuke Sato, Yoshitaka Kameya, 2008, Annals of Mathematics and Artificial Intelligence.

Kinetic Models and Qualitative Abstraction for Relational Learning in Systems Biology

Katsumi Inoue, Andrei Doncescu, Gabriel Synnaeve, 2011, BIOINFORMATICS.

Variational Bayes via Propositionalization

Kenichi Kurihara, Taisuke Sato, Yoshitaka Kameya, 2007, Probabilistic, Logical and Relational Learning - A Further Synthesis.

Using WFSTs for Efficient EM Learning of Probabilistic CFGs and Their Extensions (Special Issue : "Collection of Best Annual Papers" Organized for the 20th Anniversary of the Association for Natural Language Processing)

Taisuke Sato, Yoshitaka Kameya, Takashi Mori, 2014 .

Bottom-Up Cell Suppression that Preserves the Missing-at-random Condition

Kentaro Hayashi, Yoshitaka Kameya, Yoshitaka Kameya, 2016, TrustBus.

Negation Elimination for Finite PCFGs

Taisuke Sato, Yoshitaka Kameya, 2004, LOPSTR.

Parameter Learning of Logic Programs for Symbolic-Statistical Modeling pdf

Yoshitaka Kameya, Taisuke Sato, Taisuke Sato, 2001, J. Artif. Intell. Res..

Contrastive Relevance Propagation for Interpreting Predictions by a Single-Shot Object Detector

Naoki Mitsumoto, Yoshitaka Kameya, Yosuke Shinya, 2019, 2019 International Joint Conference on Neural Networks (IJCNN).

A Graphical Method for Parameter Learning of Symbolic-Statistical Models

Taisuke Sato, Yoshitaka Kameya, Nobuhisa Ueda, 1999, Discovery Science.

Yet More Efficient EM Learning for Parameterized Logic Programs by Inter-Goal Sharing

Neng-Fa Zhou, Taisuke Sato, Yoshitaka Kameya, 2004, ECAI.

An Exhaustive Covering Approach to Parameter-Free Mining of Non-redundant Discriminative Itemsets

Yoshitaka Kameya, Yoshitaka Kameya, 2016, DaWaK.

PRISM: A Language for Symbolic-Statistical Modeling

Taisuke Sato, Yoshitaka Kameya, Taisuke Sato, 1997, IJCAI.

Mode-Directed Tabling for Dynamic Programming, Machine Learning, and Constraint Solving

Neng-Fa Zhou, Taisuke Sato, Yoshitaka Kameya, 2010, 2010 22nd IEEE International Conference on Tools with Artificial Intelligence.

New Advances in Logic-Based Probabilistic Modeling by PRISM

Taisuke Sato, Yoshitaka Kameya, Taisuke Sato, 2008, Probabilistic Inductive Logic Programming.

PRISM : A Logic Programming Language and System for Probabilistic Modeling

Neng-Fa Zhou, Taisuke Sato, Yoshitaka Kameya, 2007 .

Discretized Kinetic Models for Abductive Reasoning in Systems Biology

Katsumi Inoue, Andrei Doncescu, Taisuke Sato, 2011, BIOSTEC.

Applying Genetic Programming with Substructure Discovery to a Traffic Signal Control Problem

Taisuke Sato, Yoshitaka Kameya, Juncichi Kumagai, 2007 .

An EM Algorithm on BDDs with Order Encoding for Logic-based Probabilistic Models

Taisuke Sato, Shin-ichi Minato, Masakazu Ishihata, 2010, ACML.

Dynamic Re-ordering in Mining Top-k Productive Discriminative Patterns

Yoshitaka Kameya, Ken'ya Ito, Yoshitaka Kameya, 2017, 2017 Conference on Technologies and Applications of Artificial Intelligence (TAAI).

A Logic-based Approach to Generatively Defined Discriminative Modeling pdf

Taisuke Sato, Yoshitaka Kameya, Keiichi Kubota, 2014, ArXiv.

Pattern-based preservation of building blocks in genetic algorithms

Yoshitaka Kameya, Chativit Prayoonsri, Yoshitaka Kameya, 2011, 2011 IEEE Congress of Evolutionary Computation (CEC).

Discovering Concepts from Word Co-occurrences with a Relational Model

Kenichi Kurihara, Taisuke Sato, Yoshitaka Kameya, 2007 .

Learning through failure

Taisuke Sato, Yoshitaka Kameya, Taisuke Sato, 2005, Probabilistic, Logical and Relational Learning.

Generative Modeling with Failure in PRISM

Neng-Fa Zhou, Taisuke Sato, Yoshitaka Kameya, 2005, IJCAI.

RP-growth: Top-k Mining of Relevant Patterns with Minimum Support Raising

Taisuke Sato, Yoshitaka Kameya, Taisuke Sato, 2012, SDM.

Associative Classification using Common Instances among Conflicting Discriminative Patterns

Yoshitaka Kameya, Kentaro Maeda, Yoshitaka Kameya, 2019, 2019 International Conference on Technologies and Applications of Artiﬁcial Intelligence (TAAI).

Accelerating genetic programming by frequent subtree mining

Yoshitaka Kameya, Yoshiaki Kurata, Junichi Kumagai, 2008, GECCO '08.

Efficient EM Learning with Tabulation for Parameterized Logic Programs

Taisuke Sato, Yoshitaka Kameya, 2000, Computational Logic.

A Separate-and-Learn Approach to EM Learning of PCFGs

Taisuke Sato, Yoshitaka Kameya, Shigeru Abe, 2001, NLPRS.

Efficient Grammar Induction Algorithm with Parse Forests from Real Corpora

Kenichi Kurihara, Taisuke Sato, Yoshitaka Kameya, 2004 .

Time Series Discretization via MDL-Based Histogram Density Estimation

Yoshitaka Kameya, 2011, 2011 IEEE 23rd International Conference on Tools with Artificial Intelligence.

CHR(PRISM)-based probabilistic logic learning

Wannes Meert, Jon Sneyers, Joost Vennekens, 2010, Theory Pract. Log. Program..

Abstracting a Human's Decision Process by PRISM

Taisuke Sato, Yoshitaka Kameya, 1998, Discovery Science.

Verbal Characterization of Probabilistic Clusters Using Minimal Discriminative Propositions

Taisuke Sato, Yoshitaka Kameya, Satoru Nakamura, 2011, 2011 IEEE 23rd International Conference on Tools with Artificial Intelligence.

Propositionalizing the EM algorithm by BDDs

Taisuke Sato, Masakazu Ishihata, Yoshitaka Kameya, 2010 .

Towards Efficient Discriminative Pattern Mining in Hybrid Domains pdf

Yoshitaka Kameya, Yoshitaka Kameya, 2019, ArXiv.

Depth-First Traversal over a Mirrored Space for Non-redundant Discriminative Itemsets

Yoshitaka Kameya, Hiroki Asaoka, Yoshitaka Kameya, 2013, DaWaK.

A Cloud Education Environment to Support Self-Learning at Home - Analysis of Self-Learning Styles from Log Data

Tomoichi Takahashi, Hidekazu Suzuki, Yoshitaka Kameya, 2015, 2015 IIAI 4th International Congress on Advanced Applied Informatics.

A Viterbi-like algorithm and EM learning for statistical abduction

Taisuke Sato, Yoshitaka Kameya, Taisuke Sato, 2000 .

Variational Bayes Inference for Logic-Based Probabilistic Models on BDDs

Taisuke Sato, Masakazu Ishihata, Yoshitaka Kameya, 2011, ILP.

Understanding the Reason for Misclassification by Generating Counterfactual Images

Takuro Kutsuna, Yoshitaka Kameya, Muneaki Suzuki, 2021, 2021 17th International Conference on Machine Vision and Applications (MVA).

Shared-Memory Parallelization of FP-growth with Dynamic Load Estimation and Balancing

Yoshitaka Kameya, Kentaro Sakurai, 2021, 2021 IEEE 12th International Workshop on Computational Intelligence and Applications (IWCIA).

A Bayesian Hybrid Approach to Unsupervised Time Series Discretization

Andrei Doncescu, Yoshitaka Kameya, Gabriel Synnaeve, 2010, 2010 International Conference on Technologies and Applications of Artificial Intelligence.

COMPUTATION OF PROBABILISTIC RELATIONSHIP BETWEEN CONCEPTS AND THEIR ATTRIBUTES USING A STATISTICAL ANALYSIS OF JAPANESE CORPORA

Yoshitaka Kameya, Yoshitaka Kameya, 2005 .

A Frequency-based Stochastic Blockmodel

Kenichi Kurihara, Yoshitaka Kameya, Taisuke Sato, 2006 .