Publications

Preprints

  1. Sugiyama, M., Tsuda, K., Nakahara, H.:
    Transductive Boltzmann Machines,
    arXiv:1805.07938.

Books

  1. Kobayashi, S., Sugiyama, M.:
    Statistical Analysis for Successful Research in Life Science (in Japanese),
    Kodansha, 2009.

Reviews

  1. Giuliano, L., Dorman, C., Bowler, C., Sugiyama, M., Vezzulli, L., Czerucka, D., Le Roux, F., D'Auria, G., Troussellier, M., Briand, F.:
    Searching for Bacterial Pathogens in the Digital Ocean—Executive Summary,
    CIESM Workshop Monograph n°49, 5—25, 2017.

    • BibTeX

  2. Sugiyama, M.:
    Finding Statistically Significant Patterns from Data,
    CIESM Workshop Monograph n°49, 53—58, 2017.

    • BibTeX

  3. Sugiyama, M.:
    Pattern Mining with Statistical Significance (in Japanese),
    Communications of the Operations Research Society of Japan, 62(4), 2017.

Journals

  1. Bellodi, E., Sato, K., Sugiyama, M.:
    Summarizing Significant Subgraphs by Probabilistic Logic Programming,
    Intelligent Data Analysis, 2019 (in press)
  2. Sugiyama, M., Ghisu, E., Llinares-López, F., Borgwardt, K.M.:
    graphkernels: R and Python Packages for Graph Comparison,
    Bioinformatics, 34(3), 530—532, 2018
  3. Llinares-López, F., Grimm, D.G., Bodenham, D.A., Gieraths, U., Sugiyama, M., Rowan, B., Borgwardt, K.M.:
    Genome-Wide Detection of Intervals of Genetic Heterogeneity Associated with Complex Traits,
    Bioinformatics, 31(12), i240—i249, 2015 (Proceedings of ISMB/ECCB 2015).
  4. Azencott, C.-A., Grimm, D.G., Sugiyama, M., Kawahara, Y., Borgwardt, K.M.:
    Efficient Network-Guided Multi-Locus Association Mapping with Graph Cut,
    Bioinformatics, 29(13), i171—i179, 2013 (Proceedings of ISMB/ECCB 2013).
  5. Sugiyama, M., Yamamoto, A.:
    Semi-Supervised Learning on Closed Set Lattices,
    Intelligent Data Analysis, 17(3), 399—421, 2013.
  6. Sugiyama, M., Hirowatari, E., Tsuiki, H., Yamamoto, A.:
    Learning Figures with the Hausdorff Metric by Fractals—Towards Computable Binary Classification,
    Machine Learning, 90(1), 91—126, 2013.
  7. Otaki, K., Sugiyama, M., Yamamoto, A.:
    Privacy Preserving Using Dummy Data for Set Operations in Itemset Mining Implemented with ZDDs,
    IEICE Trans. Inf. & Syst., E95-D(12), 3017—3025, 2012.
  8. Sugiyama, M., Imajo, K., Otaki, K., Yamamoto, A.:
    Semi-Supervised Ligand Finding Using Formal Concept Analysis,
    IPSJ TOM, 5(2), 39—48, 2012.

Conferences (reviewed)

  1. Sugiyama, M., Borgwardt, K.M.:
    Finding Statistically Significant Interactions between Continuous Features,
    IJCAI 2019.
  2. Luo, S., Sugiyama, M.:
    Bias-Variance Trade-Off in Hierarchical Probabilistic Models Using Higher-Order Feature Interactions,
    AAAI 2019.
  3. Sugiyama, M., Nakahara, H., Tsuda, K.:
    Legendre Decomposition for Tensors,
    NeurIPS 2018 (spotlight, acceptance rate = 3.5%).
  4. Sugiyama, M., Nakahara, H., Tsuda, K.:
    Tensor Balancing on Statistical Manifold,
    ICML 2017.
  5. Sugiyama, M., Nakahara, H., Tsuda, K.:
    Information Decomposition on Structured Space,
    IEEE ISIT 2016.
  6. Sugiyama, M., Borgwardt, K.M.:
    Halting in Random Walk Kernels,
    NIPS 2015.
  7. Llinares-López, F., Sugiyama, M., Papaxanthos, L., Borgwardt, K.M.:
    Fast and Memory-Efficient Significant Pattern Mining via Permutation Testing,
    ACM KDD 2015.
  8. Llinares-López, F., Grimm, D.G., Bodenham, D.A., Gieraths, U., Sugiyama, M., Rowan, B., Borgwardt, K.M.:
    Genome-Wide Detection of Intervals of Genetic Heterogeneity Associated with Complex Traits,
    ISMB/ECCB 2015. (Proceedings appear in Bioinformatics)
  9. Sugiyama, M., Llinares-López, F., Kasenburg, N., Borgwardt, K.M.:
    Significant Subgraph Mining with Multiple Testing Correction,
    SIAM SDM 2015.
  10. Sugiyama, M., Azencott, C.-A., Grimm, D.G., Kawahara, Y., Borgwardt, K.M.:
    Multi-Task Feature Selection on Multiple Networks via Maximum Flows,
    SIAM SDM 2014.
  11. Sugiyama, M., Borgwardt, K.M.:
    Rapid Distance-Based Outlier Detection via Sampling,
    NIPS 2013.
  12. Azencott, C.-A., Grimm, D.G., Sugiyama, M., Kawahara, Y., Borgwardt, K.M.:
    Efficient Network-Guided Multi-Locus Association Mapping with Graph Cut,
    ISMB/ECCB 2013. (Proceedings appear in Bioinformatics)
  13. Sugiyama, M., Borgwardt, K.M.:
    Measuring Statistical Dependence via the Mutual Information Dimension,
    IJCAI 2013.
  14. Sugiyama, M., Yamamoto, A.:
    A Fast and Flexible Clustering Algorithm Using Binary Discretization,
    IEEE ICDM 2011.
  15. Sugiyama, M., Yamamoto, A.:
    The Minimum Code Length for Clustering Using the Gray Code,
    ECML PKDD 2011 (LNCS 6913).
  16. Sugiyama, M., Yamamoto, A.:
    Semi-Supervised Learning for Mixed-Type Data via Formal Concept Analysis,
    ICCS 2011 (LNCS 6828).

    (There is an extended journal version)

  17. Sugiyama, M., Yamamoto, A.:
    The Coding Divergence for Measuring the Complexity of Separating Two Sets,
    ACML 2010 (JMLR Workshop and Conference Proceedings 13).
  18. Sugiyama, M., Hirowatari, E., Tsuiki, H., Yamamoto, A.:
    Learning Figures with the Hausdorff Metric by Fractals,
    ALT 2010 (LNCS 6331).

    (There is an extended journal version)

Workshops (reviewed)

  1. Yoneda, Y., Sugiyama, M., Washio, T.:
    Learning Graph Representation via Formal Concept Analysis,
    NeurIPS 2018 R2L.

    • BibTeX

  2. Baba, Y., Sugiyama, M., Washio, T.:
    Finding Combinations of Binary Variables with Guaranteed Accuracy,
    NIPS 2016 ADAPTIVE.

    • BibTeX

  3. Llinares-López, F., Sugiyama, M., Papaxanthos, L., Borgwardt, K.M.:
    Fast and Memory-Efficient Significant Pattern Mining via Permutation Testing,
    BIOKDD'15.

    • BibTeX

  4. Sugiyama, M., Otaki, K.:
    Detecting Anomalous Subgraphs on Attributed Graphs via Parametric Flow,
    GABA 2014 (LNCS 9067).
  5. Sugiyama, M., Borgwardt, K.M.:
    Rapid Distance-Based Outlier Detection via Sampling,
    RMML 2013.

    • BibTeX

  6. Sugiyama, M.:
    Outliers on Concept Lattices,
    DDS13 (LNCS 8417).
  7. Sugiyama, M., Yoshioka, T., Yamamoto, A.:
    High-throughput Data Stream Classification on Trees,
    ALSIP 2011.

    • BibTeX

  8. Sugiyama, M., Yamamoto, A.:
    Fast Clustering Based on the Gray-Code,
    LLLL 2011.

    • BibTeX

  9. Sugiyama, M., Hirowatari, E., Tsuiki, H., Yamamoto, A.:
    Learning Figures with the Hausdorff Metric by Self-similar Sets,
    LLLL 2009.

    • BibTeX

  10. Sugiyama, M., Hirowatari, E., Tsuiki, H., Yamamoto, A.:
    Learning from Real-Valued Data with the Model Inference Mechanism through the Gray-Code Embedding,
    LLLL 2006.

    • BibTeX

Conferences (non-reviewed)

  1. Sugiyama, M.:
    Significant Pattern Mining on Graphs,
    MCP 2017.
  2. Sugiyama, M., Imajo, K., Otaki, K., Yamamoto, A.:
    Discovering Ligands for TRP Ion Channels Using Formal Concept Analysis,
    ILP 2011. (There is an extended journal version)
  3. Kawai, Y., Sugiyama, M., Yamamoto, A.:
    Mining RNA Families with Structure Histograms,
    DMSS 2011.

Thesis

Mahito Sugiyama,
Studies on Computational Learning via Discretization
PDF
Slide

Doctoral thesis, Graduate School of Informatics, Kyoto University, March, 2012.
Mahito Sugiyama,
Cooling-induced Shivering-like Response in Zebrafish
Master thesis, Graduate School of Informatics, Kyoto University, March, 2008.
Mahito Sugiyama,
Learning Real-Valued Functions using Gray Codes of Real Numbers and revising Model Inference Mechanism (in Japanese),
Bachelor thesis, The School of Informatics and Mathematical Science, Faculty of Engineering, Kyoto University, March, 2006.

© Mahito Sugiyama 2019

Searching for bacterial pathogens in the Digital Ocean—Executive Summary
@article{Sugiyama2017CIESM1,
Author = {Giuliano, L. and Dorman, C. and Bowler, C. and Sugiyama, M. and Vezzulli, L. and Czerucka, D. and Le Roux, F. and D'Auria, G. and Troussellier, M. and Briand, F.},
Title = {Searching for bacterial pathogens in the Digital Ocean---Executive Summary},
Journal = {CIESM Workshop Monograph},
Volume = {49},
Pages = {5--25},
Year = {2017}}
Finding Statistically Significant Patterns from Data
@article{Sugiyama2017CIESM2,
Author = {Sugiyama, M.},
Title = {Finding Statistically Significant Patterns from Data},
Journal = {CIESM Workshop Monograph},
Volume = {49},
Pages = {53--58},
Year = {2017}}
Pattern Mining with Statistical Significance (in Japanese)
@article{Sugiyama2017OS,
Author = {Sugiyama, M.},
Title = {Pattern Mining with Statistical Significance (in Japanese)},
Journal = {Communications of the Operations Research Society of Japan},
Volume = {62},
Number = {4},
Pages = {226--232},
Year = {2017}}
graphkernels: R and Python Packages for Graph Comparison
@article{Sugiyama2018Bioinfo,
Author = {Sugiyama, M. and Ghisu, E. and Llinares-L{\'o}pez, F. and Borgwardt, K. M.},
Title = {graphkernels: R and Python Packages for Graph Comparison},
Journal = {Bioinformatics},
Volume = {34},
Number = {3},
Pages = {530--532},
Year = {2018}}
Genome-Wide Detection of Intervals of Genetic Heterogeneity Associated with Complex Traits
@article{Llinares2015ISMB,
Author = {Llinares-L{\'o}pez, F. and Grimm, D. G. and Bodenham, D. A. and Gieraths, U. and Sugiyama, M. and Rowan, B. and Borgwardt, K. M.},
Title = {Genome-Wide Detection of Intervals of Genetic Heterogeneity Associated with Complex Traits},
Journal = {Bioinformatics},
Volume = {31},
Number = {12},
Pages = {i240--i249},
Year = {2015}}
Efficient Network-Guided Multi-Locus Association Mapping with Graph Cut
@article{Azencott2013ISMB,
Author = {Azencott, C.-A. and Grimm, D. G. and Sugiyama, M. and Kawahara, Y. and Borgwardt, K. M.},
Title = {Efficient Network-Guided Multi-Locus Association Mapping with Graph Cut},
Journal = {Bioinformatics},
Volume = {29},
Number = {13},
Pages = {i171--i179},
Year = {2013}}
Semi-Supervised Learning on Closed Set Lattices
@article{SugiyamaIDA01,
Author = {Sugiyama, M. and Yamamoto, A.},
Title = {Semi-Supervised Learning on Closed Set Lattices},
Journal = {Intelligent Data Analysis},
Volume = {17},
Number = {3},
Pages = {399--421},
Publisher = {IOS Press},
Year = {2013}}
Learning Figures with the Hausdorff Metric by Fractals—Towards Computable Binary Classification
@article{SugiyamaMLJ01,
Author = {Sugiyama, M. and Hirowatari, E. and Tsuiki, H. and Yamamoto, A.},
Title = {Learning Figures with the Hausdorff Metric by Fractals---Towards Computable Binary Classification},
Journal = {Machine Learning},
Volume = {90},
Number = {1},
Pages = {91--126},
Publisher = {Springer},
Year = {2013}}
Privacy Preserving Using Dummy Data for Set Operations in Itemset Mining Implemented with ZDDs
@article{OtakiIEICE01,
Author = {Otaki, K. and Sugiyama, M. and Yamamoto, A.},
Title = {Privacy Preserving Using Dummy Data for Set Operations in Itemset Mining Implemented with {ZDD}s},
Journal = {IEICE Transactions on Information and Systems},
Volume = {E95-D},
Number = {12},
Pages = {3017--3025},
Publisher = {IEICE},
Year = {2012}}
Semi-Supervised Ligand Finding Using Formal Concept Analysis
@article{SugiyamaTOM01,
Author = {Sugiyama, M. and Imajo, K. and Otaki, K. and Yamamoto, A.},
Title = {Semi-Supervised Ligand Finding Using Formal Concept Analysis},
Journal = {IPSJ Transactions on Mathematical Modeling and Its Applications (TOM)},
Volume = {5},
Number = {2},
Pages = {39--48},
Publisher = {The Information Processing Society of Japan (IPSJ)},
Year = {2012}}
Finding Statistically Significant Interactions between Continuous Features
@inproceedings{Sugiyama2019Finding,
Author = {Sugiyama, M. and Borgwardt, K},
Title = {Finding Statistically Significant Interactions between Continuous Features},
Booktitle = {Proceedings of the 28th International Joint Conference on Artificial Intelligence (IJCAI 2019)},
Pages = {3490--3498},
Address = {Macao, China},
Month = {August},
Year = {2019}}
Bias-Variance Trade-Off in Hierarchical Probabilistic Models Using Higher-Order Feature Interactions
@inproceedings{Luo2019AAAI,
Author = {Luo, S., Sugiyama, M.},
Title = {Bias-Variance Trade-Off in Hierarchical Probabilistic Models Using Higher-Order Feature Interactions},
Booktitle = {Proceedings of the 33rd AAAI Conference on Artificial Intelligence (AAAI-19)},
Volume = {33},
Number = {01},
Pages = {4488--4495},
Address = {Hawaii, USA},
Month = {January--February},
Year = {2019}}
Legendre Decomposition for Tensors
@inproceedings{Sugiyama2018NeurIPS,
Author = {Sugiyama, M. and Nakahara, H. and Tsuda, K.},
Title = {Legendre Decomposition for Tensors},
Booktitle = {Advances in Neural Information Processing Systems 31},
Pages = {8825--8835},
Address = {Montréal, Canada},
Month = {December},
Year = {2018}}
Tensor Balancing on Statistical Manifold
@inproceedings{Sugiyama2017ICML,
Author = {Sugiyama, M. and Nakahara, H. and Tsuda, K.},
Title = {Tensor Balancing on Statistical Manifold},
Booktitle = {Proceedings of the 34th International Conference on Machine Learning (ICML)},
Volume = {70},
Pages = {3270--3279},
Series = {Proceedings of Machine Learning Research},
Address = {Sydney, Australia},
Month = {August},
Year = {2017}}
Information Decomposition on Structured Space
@inproceedings{Sugiyama2016ISIT,
Author = {Sugiyama, M. and Nakahara, H. and Tsuda, K.},
Title = {Information Decomposition on Structured Space},
Booktitle = {2016 IEEE International Symposium on Information Theory (ISIT)},
Pages = {575--579},
Address = {Barcelona, Spain},
Month = {July},
Year = {2016}}
Halting in Random Walk Kernels
@inproceedings{Sugiyama2015NIPS,
Author = {Sugiyama, M. and Borgwardt, K. M.},
Title = {Halting in Random Walk Kernels},
Booktitle = {Advances in Neural Information Processing Systems 28},
Pages = {1630--1638},
Address = {Montréal, Canada},
Month = {December},
Year = {2015}}
Fast and Memory-Efficient Significant Pattern Mining via Permutation Testing
@inproceedings{Llinares2015KDD,
Author = {Llinares-L{\'o}pez, F. and Sugiyama, M. and Papaxanthos, L. and Borgwardt, K. M.},
Title = {Fast and Memory-Efficient Significant Pattern Mining via Permutation Testing},
Booktitle = {Proceedings of the 21st ACM SIGKDD Conference on Knowledge Discovery and Data Mining},
Pages = {725--734},
Address = {Sydney, Australia},
Month = {August},
Year = {2015}}
Significant Subgraph Mining with Multiple Testing Correction
@inproceedings{Sugiyama2015SDM,
Author = {Sugiyama, M. and Llinares-L{\'o}pez, F. and Kasenburg, N. and Borgwardt, K. M.},
Title = {Significant Subgraph Mining with Multiple Testing Correction},
Booktitle = {Proceedings of the 2015 SIAM International Conference on Data Mining},
Pages = {37--45},
Address = {Vancouver, British Columbia, Canada},
Month = {April--May},
Year = {2015}}
Multi-Task Feature Selection on Multiple Networks via Maximum Flows
@inproceedings{Sugiyama2014SDM,
Author = {Sugiyama, M. and Azencott, C.-A. and Grimm, D. G. and Kawahara, Y. and Borgwardt, K. M.},
Title = {Multi-Task Feature Selection on Multiple Networks via Maximum Flows},
Booktitle = {Proceedings of the 2014 SIAM International Conference on Data Mining},
Pages = {199--207},
Address = {Philadelphia, Pennsylvania, USA},
Month = {April},
Year = {2014}}
Rapid Distance-Based Outlier Detection via Sampling
@inproceedings{Sugiyama2013NIPS,
Author = {Sugiyama, M. and Borgwardt, K. M.},
Title = {Rapid Distance-Based Outlier Detection via Sampling},
Booktitle = {Advances in Neural Information Processing Systems 26},
Pages = {467--475},
Address = {Lake Tahoe, Nevada, USA},
Month = {December},
Year = {2013}}
Measuring Statistical Dependence via the Mutual Information Dimension
@inproceedings{Sugiyama2013IJCAI,
Author = {Sugiyama, M. and Borgwardt, K. M.},
Title = {Measuring Statistical Dependence via the Mutual Information Dimension},
Booktitle = {Proceedings of the 23rd International Joint Conference on Artificial Intelligence (IJCAI 2013)},
Pages = {1692--1698},
Address = {Beijing, China},
Month = {August},
Year = {2013}}
A Fast and Flexible Clustering Algorithm Using Binary Discretization
@inproceedings{Sugiyama2011ICDM,
Author = {Sugiyama, M. and Yamamoto, A.},
Title = {A Fast and Flexible Clustering Algorithm Using Binary Discretization},
Booktitle = {Proceedings of the 2011 IEEE International Conference on Data Mining (ICDM 2011)},
Pages = {1212--1217},
Address = {Vancouver, Canada},
Month = {December},
Year = {2011}}
The Minimum Code Length for Clustering Using the Gray Code
@inproceedings{Sugiyama2011ECML,
Author = {Sugiyama, M. and Yamamoto, A.},
Title = {The Minimum Code Length for Clustering Using the {G}ray Code},
Editor = {Gunopulos, D. and Hofmann, T. and Malerba, D. and Vazirgiannis, M.},
Booktitle = {Machine Learning and Knowledge Discovery in Databases},
Series = {Lecture Notes in Computer Science},
Volume = {6913},
Pages = {365--380},
Publisher = {Springer},
Year = {2011}}
Semi-Supervised Learning for Mixed-Type Data via Formal Concept Analysis
@inproceedings{Sugiyama2011ICCS,
Author = {Sugiyama, M. and Yamamoto, A.},
Title = {Semi-Supervised Learning for Mixed-Type Data via Formal Concept Analysis},
Editor = {Andrews, S. and Polovina, S. and Hill, R. and Akhgar, B.},
Booktitle = {Conceptual Structures for Discovering Knowledge},
Series = {Lecture Notes in Computer Science},
Volume = {6828},
Pages = {284--297},
Publisher = {Springer},
Year = {2011}}
The Coding Divergence for Measuring the Complexity of Separating Two Sets
@inproceedings{Sugiyama2010ACML,
Author = {Sugiyama, M. and Yamamoto, A.},
Title = {The Coding Divergence for Measuring the Complexity of Separating Two Sets},
Editor = {Sugiyama, M. and Yang, Q.},
Booktitle = {Proceedings of 2nd Asian Conference on Machine Learning (ACML2010)},
Series = {JMLR Workshop and Conference Proceedings},
Volume = {13},
Pages = {127--143},
Address = {Tokyo, Japan},
Month = {November},
Year = {2010}}
Learning Figures with the Hausdorff Metric by Fractals
@inproceedings{Sugiyama2010ALT,
Author = {Sugiyama, M. and Hirowatari, E. and Tsuiki, H. and Yamamoto, A.},
Title = {Learning Figures with the {H}ausdorff Metric by Fractals},
Editor = {Hutter, M. and Stephan, F. and Vovk, V. and Zeugmann, T.},
Booktitle = {Algorithmic Learning Theory},
Series = {Lecture Notes in Computer Science},
Volume = {6331},
Pages = {315--329},
Publisher = {Springer},
Year = {2010}}
Learning Graph Representation via Formal Concept Analysis
@inproceedings{Yoneda2018R2L,
Author = {Yoneda, Y. and Sugiyama, M. and Washio, T.},
Title = {Learning Graph Representation via Formal Concept Analysis},
Booktitle = {Proceedings of Relational Representation Learning},
Address = {Montréal, Canada},
Month = {December},
Year = {2018}}
Finding Combinations of Binary Variables with Guaranteed Accuracy
@inproceedings{Baba2016ADAPTIVE,
Author = {Baba, Y. and Sugiyama, M. and Washio, T.},
Title = {Finding Combinations of Binary Variables with Guaranteed Accuracy},
Booktitle = {Proceedings of Adaptive and Scalable Nonparametric Methods in Machine Learning},
Address = {Barcelona, Spain},
Month = {December},
Year = {2016}}
Fast and Memory-Efficient Significant Pattern Mining via Permutation Testing
@inproceedings{Llinares2015BIOKDD,
Author = {Llinares-L{\'o}pez, F. and Sugiyama, M. and Papaxanthos, L. and Borgwardt, K. M.},
Title = {Fast and Memory-Efficient Significant Pattern Mining via Permutation Testing},
Booktitle = {Proceedings of 14th International Workshop on Data Mining in Bioinformatics (BIOKDD'15)},
Address = {Sydney, Australia},
Month = {August},
Year = {2015}}
Detecting Anomalous Subgraphs on Attributed Graphs via Parametric Flow
@inproceedings{Sugiyama2014GABA,
Author = {Sugiyama, M. and Otaki, K.},
Title = {Detecting Anomalous Subgraphs on Attributed Graphs via Parametric Flow},
Editor = {Murata, T. and Mineshima, K. and Bekki, D.},
Booktitle = {New Frontiers in Artificial Intelligence},
Series = {Lecture Notes in Computer Science},
Volume = {9067},
Pages = {340--355},
Publisher = {Springer},
Address = {Yokohama, Japan},
Year = {2015}}
Rapid Distance-Based Outlier Detection via Sampling
@inproceedings{Sugiyama2013RMML,
Author = {Sugiyama, M. and Borgwardt, K. M.},
Title = {Rapid Distance-Based Outlier Detection via Sampling},
Booktitle = {NIPS 2013 Randomized Methods for Machine Learning Workshop (RMML 2013)},
Address = {Lake Tahoe, Nevada, USA},
Month = {December},
Year = {2013}}
Outliers on Concept Lattices
@inproceedings{Sugiyama2013DDS,
Author = {Sugiyama, M.},
Title = {Outliers on Concept Lattices},
Editor = {Nakano, Y. and Satoh, K. and Bekki, D.},
Booktitle = {New Frontiers in Artificial Intelligence},
Series = {Lecture Notes in Computer Science},
Volume = {8417},
Pages = {352--368},
Publisher = {Springer},
Address = {Yokohama, Japan},
Year = {2014}}
High-throughput Data Stream Classification on Trees
@inproceedings{Sugiyama2011ALSIP,
Author = {Sugiyama, M. and Yoshioka, T. and Yamamoto, A.},
Title = {High-throughput Data Stream Classification on Trees},
Booktitle = {Proceedings of Second Workshop on Algorithms for Large-Scale Information Processing in Knowledge Discovery (ALSIP 2011)},
Address = {Kagawa, Japan},
Month = {December},
Year = {2011}}
Fast Clustering Based on the Gray-Code
@inproceedings{Sugiyama2011LLLL,
Author = {Sugiyama, M. and Yamamoto, A.},
Title = {Fast Clustering Based on the {G}ray-Code},
Booktitle = {Proceedings of 7th Workshop on Learning with Logics and Logics for Learning (LLLL2011)},
Pages = {42},
Address = {Osaka, Japan},
Month = {March},
Year = {2011}}
Learning Figures with the Hausdorff Metric by Self-similar Sets
@inproceedings{Sugiyama2009LLLL,
Author = {Sugiyama, M. and Hirowatari, E. and Tsuiki, H. and Yamamoto, A.},
Title = {Learning Figures with the {H}ausdorff Metric by Self-similar Sets},
Booktitle = {Proceedings of 6th Workshop on Learning with Logics and Logics for Learning (LLLL2009)},
Pages = {27--34},
Address = {Kyoto, Japan},
Month = {July},
Year = {2009}}
Learning from Real-Valued Data with the Model Inference Mechanism through the Gray-Code Embedding
@inproceedings{Sugiyama2006LLLL,
Author = {Sugiyama, M. and Hirowatari, E. and Tsuiki, H. and Yamamoto, A.},
Title = {Learning from Real-Valued Data with the Model Inference Mechanism through the {G}ray-Code Embedding},
Booktitle = {Proceedings of 4th Workshop on Learning with Logics and Logics for Learning (LLLL2006)},
Pages = {31--37},
Address = {Tokyo, Japan},
Month = {June},
Year = {2006}}