Yoshitaka KAMEYA
Associate Professor at
Department of Information Engineering,
Faculty of Science and Technology,
Meijo University
Research area
Machine learning and data mining
(probabilistic reasoning and learning, or pattern mining)
Contact information
 Address:
 Department of Information Engineering,
Faculty of Science and Technology,
Meijo University,
1501 Shiogamaguchi, Tenpakuku, Nagoya, Aichi 4688502, Japan
 Room:
 24071, Bldg. 2, Tenpaku Campus
 Phone:
 +81528382567
 Email:
 ykameya [at] meijou.ac.jp
Lectures
 Operating Systems
 Programming Exercises 3
 Information Engineering Experiments 2
 Algorithms and Data Structures 2
 Advanced Intelligent Data Analysis (for graduate students)
Dissertation
Kameya, Y.:
Representation and Learning of SymbolicStatistical Knowledge.
Ph.D. thesis, Tokyo Institute of Technology, 2000.
Also available as Technical Report
TR000015, Dept. of Computer Science,
Tokyo Institute of Technology, November, 2000.
(in Japanese)
Publications (peerreviewed)

Kameya, Y. and Ito, K.:
Dynamic reordering in mining topk productive discriminative patterns.
Proceedings of the 2017 Conference on Technologies and Applications for Artificial Intelligence (TAAI2017), pp.172–177, 2017.
[paper] (IEEE Xplore)
[paper] (Selfarchive)
[slides]

Kameya, Y.:
An exhaustive covering approach to parameterfree mining of nonredundant discriminative itemsets.
Proceedings of the 18th International Conference on Big Data Analytics and Knowledge Discovery (DaWaK2016),
pp.143–159, 2016.
[paper] (Springer)
[paper] (Selfarchive)
[slides]

Kameya, Y. and Hayashi, K.:
Bottomup cell suppression that preserves the missingatrandom condition.
Proceedings of the 13th International Conference on Trust, Privacy, and Security in Digital Business (TrustBus2016),
pp.65–78, 2016.
[paper] (Springer)
[paper] (Selfarchive)
[slides]

Takahashi, T., Asahi, K., Suzuki, H., Kawasumi, M. and Kameya, Y.:
A cloud education environment to support selflearning at home — Analysis of selflearning styles from log data.
Proceedings of the 2015 IIAI 4th International Conference on Advanced Applied Informatics (IIAIAAI2015),
pp.437–440, 2015. [paper] (IEEE Xplore)

Kameya, Y., Mori, T. and Sato, T.:
Using WFSTs for efficient EM learning of probabilistic CFGs and their extensions.
Journal of Natural Language Processing, Vol.21, No.4,
pp.619–658, 2014 (English translation of our Japanese paper published in 2001, for celebrating the 20th anniversary of ANLP).
[paper] (JSTAGE)

Kameya, Y. and Asaoka, H.:
Depthfirst traversal over a mirrored space for nonredundant discriminative itemsets.
Proceedings of the 15th International Conference on Data Warehousing and Knowledge Discovery (DaWaK2013),
pp.196–208, 2013.
[paper] (Springer)
[paper] (Selfarchive)
[slides]

Kameya, Y. and Sato, T.:
RPgrowth: Topk mining of relevant patterns with minimum support raising.
Proceedings of the 2012 SIAM International Conference on Data Mining (SDM2012),
pp.816–827, 2012.
[paper] (SIAM)
[poster]

Ishihata, M., Kameya, Y. and Sato, T.:
Variational Bayes inference for logicbased probabilistic models on BDDs.
Proceedings of the 21st International Conference on Inductive Logic Programming (ILP2011), pp.189–203, 2011.
[paper] (Springer)

Kameya, Y.:
Time series discretization via MDLbased histogram density estimation.
Proceedings of the 23rd IEEE International Conference on Tools with Artificial Intelligence
(ICTAI2011), pp.732–739, 2011.
[paper] (IEEE Xplore)
[paper] (Selfarchive)
[slides]

Kameya, Y., Nakamura, S., Iwasaki, T. and Sato, T.:
Verbal characterization of probabilistic clusters using minimal discriminative propositions.
Proceedings of the 23rd IEEE International Conference on Tools with Artificial Intelligence
(ICTAI2011), pp.873–875, 2011.
[paper] (Short version, IEEE Xplore)
[paper] (Short version, Selfarchive)
[paper] (Full version, ArXiv)
[poster]

Kameya, Y. and Prayoonsri, C.:
Patternbased preservation of building blocks in genetic algorithms.
Proceedings of the 2011 IEEE Congress on Evolutionary Computation (CEC2011), pp.2578–2585, 2011.
[paper] (IEEE Xplore)
[paper] (Selfarchive)
[poster]

Kameya, Y., Nakamura, S., Iwasaki, T. and Sato, T.:
Characterizing probabilistic clusters by minimal discriminative propositions.
Extended abstract at the 7th Workshop on Learning with Logics and Logics for Learning (LLLL2011), 2011.
[paper] (Selfarchive)

Synnaeve, G., Inoue, K., Doncescu, A., Nabeshima, H.,
Kameya, Y., Ishihata, M. and Sato, T.:
Kinetic models and qualitative abstraction for relational
learning in systems biology.
Proceedings of the International Conference on Bioinformatics Models, Methods and
Algorithms (BIOINFORMATICS2011),
2011.

Ishihata M., Kameya, Y., Sato, T. and Minato, S.:
An EM algorithm on BDDs with order encoding for logicbased
probabilistic models.
Proceedings of the 2nd Asian Conference on Machine Learning
(ACML2010), pp.161–176, 2010.
[paper] (PMLR)

Kameya, Y., Synnaeve, G., Doncescu, A., Inoue, K. and Sato, T.:
A Bayesian hybrid approach to unsupervised time series discretization.
Proceedings of the 2010 Conference on Technologies and Applications
of Artificial Intelligence (TAAI2010), pp.342–349, 2010.
[paper] (IEEE Xplore)
[paper] (Selfarchive)
[slides]

Zhou, N.F., Kameya, Y. and Sato, T.:
Modedirected tabling for dynamic programming, machine learning, and
constraint solving.
Proceedings of the 22nd International Conference on Tools with
Artificial Intelligence (ICTAI2010), Vol.2, pp.213–218, 2010.
[paper] (IEEE Xplore)

Sneyers, J., Meert, W., Vennekens, J., Kameya, Y. and Sato, T.:
CHR(PRISM)based probabilistic logic learning.
Theory and Practice of Logic Programming,
Vol.10, No.4–6, pp.433–447, 2010.
[paper] (Cambridge Univ. Press)

Inoue, K., Sato, T., Ishihata, M., Kameya, Y. and Nabeshima, H.:
Evaluating abductive hypotheses using an EM algorithm on BDDs.
Proceedings of the 21st International Joint Conference on Artificial Intelligence
(IJCAI2009), pp.810–815, 2009.
[paper] (Conference site)

Sato, T., Kameya, Y., Kurihara, K.:
Variational Bayes via propositionalized probability computation in PRISM.
Annals of Mathematics and Artificial Intelligence, Vol.54, No.1–3, pp.135–158, 2009.
[paper] (Springer)

Kameya, Y., Kumagai, J. and Kurata, Y.:
Accelerating genetic programming by frequent subtree mining.
Proceedings of the 2008 Genetic and Evolutionary Computation Conference (GECCO2008),
pp.1203–1210, 2008.
[paper] (ACM DL)
[paper] (Selfarchive)

Ishihata, M., Kameya, Y., Sato, T. and Minato, S.:
Propositionalizing the EM algorithm by BDDs.
Late breaking papers at the 18th International Conference on
Inductive Logic Programming (ILP2008), 2008.

Sato, T. and Kameya, Y.:
New advances in logicbased probabilistic modeling by PRISM.
In Probabilistic Inductive Logic Programming,
LNCS 4911, Springer, pp.118–155, 2008.
[paper] (Springer)

Kurihara, K., Kameya, Y. and Sato, T.:
Discovering concepts from word cooccurrences with a relational model.
Transactions of the Japanese Society for Artificial Intelligence, Vol.22, No.2,
pp.218–226, 2007.
[paper] (JSTAGE)

Izumi, Y., Kameya, Y. and Sato, T.:
Parallel EM learning for symbolicstatistical models.
Proceedings of the International Workshop on DataMining and Statistical Science
(DMSS2006),
pp.133–140, 2006.

Sato,T. and Kameya, Y.:
Negation elimination for finite PCFGs.
Proceedings of the International Symposium on
Logicbased Program Synthesis and Transformation 2004
(LOPSTR04),
later selectively published as Logicbased Program Synthesis
and Transformation,
Springer LNCS 3573,
S. Etalle (Ed.), pp.117–132, 2005.
[paper] (Springer)

Sato, T., Kameya, Y. and Zhou, N.F.:
Generative modeling with failure in PRISM.
Proceedings of the 19th International Joint Conference on
Artificial Intelligence
(IJCAI2005), pp.847–852, 2005.

Kameya, Y., Sato, T. and Zhou, N.F.:
Yet more efficient EM learning for parameterized logic programs
by intergoal sharing.
Proceedings of the 16th European Conference on Artificial Intelligence (ECAI2004), pp.490–494, 2004.
[paper] (Selfarchive)

Sato, T. and Kameya, Y.:
Statistical abduction with tabulation.
Computational Logic: Logic Programming and Beyond,
Kakas, A. and Sadri, F. (eds), pp.567–587, LNAI Vol.2408, Springer, 2002.

Sato, T. and Kameya, Y.:
Parameter learning of logic programs for symbolicstatistical modeling.
Journal of Artificial Intelligence Research
(JAIR), Vol.15, pp.391–454, 2001.

Sato, T., Abe, S., Kameya, Y., and Shirai, K.:
A separateandlearn approach to EM learning of PCFGs.
Proceedings of the 6th Natural Language Processing Pacific Rim
Symposium
(NLPRS2001),
pp.255–262, 2001.

Kameya, Y. and Sato, T.:
Efficient EM learning with tabulation for parameterized logic programs.
Proceedings of the 1st International Conference on Computational
Logic (CL2000),
LNAI Vol.1861, pp.269–294, 2000.

Kameya, Y., Ueda, N., and Sato, T.:
A graphical method for parameter learning of symbolicstatistical
models.
Proceedings of the 2nd International Conference on Discovery
Science (DS99),
LNAI Vol.1721, pp.264–276, 1999.

Ueda, N., Kameya, Y., and Sato, T.:
A parameter updating of stochastic contextfree grammars in linear time
on the number of productions.
In Proceedings of the 1st IMC workshop, 1999.

Kameya, Y. and Sato, T.:
Abstracting human's decision process by PRISM.
Proceedings of the 1st International Conference on Discovery
Science (DS98), pp.389–390, 1998.

Sato, T. and Kameya, Y.:
PRISM: A symbolicstatistical modeling language.
Proceedings of the 15th International Joint Conference on Artificial
Intelligence (IJCAI97),
pp.1330–1335, 1997.
Publications (unreviewed)

Sato, T., Kubota, K. and Kameya, Y.:
Logicbased Approach to Generatively Defined Discriminative Modeling, arXiv:1410.3935, October, 2014 (Previously presented at ILP2013).

Kameya, Y., Nakamura, S., Iwasaki, T. and Sato, T.:
Verbal Characterization of Probabilistic Clusters using Minimal Discriminative Propositions,
arXiv:1108.5002,
August, 2011.

Ishihata, M., Kameya, Y., Sato, T. and Minato, S.:
Propositionalizing the EM algorithm by BDDs.
Late breaking papers at the 18th International Conference on
Inductive Logic Programming (ILP2008), 2008.

Sato, T. and Kameya, Y.:
Learning through failure.
Dagstuhl Seminar Proceedings on Probabilistic, Logical and
Relational Learning  Towards a Synthesis, 2006.

Sato, T. and Kameya, Y.:
A Viterbilike algorithm and EM learning for statistical abduction.
Proceedings of UAI2000 Workshop on Fusion of Domain Knowledge with Data
for Decision Support, 2000.
Software
 PRISM — Prologbased programming language for probabilistic modeling
 NBCTK — Generalpurpose probabilistic clustering tool
Links
Last update: May 26, 2018