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Hisashi Kashima: [Publications] [Author Rank by year] [Co-authors] [Prefers] [Cites] [Cited by]

Publications of Author

  1. Hisashi Kashima, Tadashi Tsumura, Tsuyoshi Idé, Takahide Nogayama, Ryo Hirade, Hiroaki Etoh, Takeshi Fukuda
    Network-Based Problem Detection for Distributed Systems. [Citation Graph (0, 0)][DBLP]
    ICDE, 2005, pp:978-989 [Conf]
  2. Akihiro Inokuchi, Hisashi Kashima
    Mining Significant Pairs of Patterns from Graph Structures with Class Labels. [Citation Graph (0, 0)][DBLP]
    ICDM, 2003, pp:83-90 [Conf]
  3. Hisashi Kashima, Naoki Abe
    A Parameterized Probabilistic Model of Network Evolution for Supervised Link Prediction. [Citation Graph (0, 0)][DBLP]
    ICDM, 2006, pp:340-349 [Conf]
  4. Hisashi Kashima, Teruo Koyanagi
    Kernels for Semi-Structured Data. [Citation Graph (0, 0)][DBLP]
    ICML, 2002, pp:291-298 [Conf]
  5. Hisashi Kashima, Yuta Tsuboi
    Kernel-based discriminative learning algorithms for labeling sequences, trees, and graphs. [Citation Graph (0, 0)][DBLP]
    ICML, 2004, pp:- [Conf]
  6. Hisashi Kashima, Koji Tsuda, Akihiro Inokuchi
    Marginalized Kernels Between Labeled Graphs. [Citation Graph (0, 0)][DBLP]
    ICML, 2003, pp:321-328 [Conf]
  7. Tsuyoshi Idé, Hisashi Kashima
    Eigenspace-based anomaly detection in computer systems. [Citation Graph (0, 0)][DBLP]
    KDD, 2004, pp:440-449 [Conf]
  8. Hisashi Kashima
    Risk-Sensitive Learning via Expected Shortfall Minimization. [Citation Graph (0, 0)][DBLP]
    SDM, 2006, pp:- [Conf]
  9. Tetsuo Shibuya, Hisashi Kashima, Akihiko Konagaya
    Efficient filtering methods for clustering cDNAs with spliced sequence alignment. [Citation Graph (0, 0)][DBLP]
    Bioinformatics, 2004, v:20, n:1, pp:29-39 [Journal]

  10. Training Conditional Random Fields Using Incomplete Annotations. [Citation Graph (, )][DBLP]


  11. Inlier-Based Outlier Detection via Direct Density Ratio Estimation. [Citation Graph (, )][DBLP]


  12. A Linear-Time Graph Kernel. [Citation Graph (, )][DBLP]


  13. Nonparametric Return Distribution Approximation for Reinforcement Learning. [Citation Graph (, )][DBLP]


  14. A Fast Augmented Lagrangian Algorithm for Learning Low-Rank Matrices. [Citation Graph (, )][DBLP]


  15. K-means clustering of proportional data using L1 distance. [Citation Graph (, )][DBLP]


  16. Regression with interval output values. [Citation Graph (, )][DBLP]


  17. A new objective function for sequence labeling. [Citation Graph (, )][DBLP]


  18. Least absolute policy iteration for robust value function approximation. [Citation Graph (, )][DBLP]


  19. Multi-Task Learning via Conic Programming. [Citation Graph (, )][DBLP]


  20. Direct Importance Estimation with Model Selection and Its Application to Covariate Shift Adaptation. [Citation Graph (, )][DBLP]


  21. Unsupervised Change Analysis Using Supervised Learning. [Citation Graph (, )][DBLP]


  22. On Pairwise Kernels: An Efficient Alternative and Generalization Analysis. [Citation Graph (, )][DBLP]


  23. Finding Itemset-Sharing Patterns in a Large Itemset-Associated Graph. [Citation Graph (, )][DBLP]


  24. Fast and Scalable Algorithms for Semi-supervised Link Prediction on Static and Dynamic Graphs. [Citation Graph (, )][DBLP]


  25. Direct Density Ratio Estimation for Large-scale Covariate Shift Adaptation. [Citation Graph (, )][DBLP]


  26. Integration of Multiple Networks for Robust Label Propagation. [Citation Graph (, )][DBLP]


  27. Roughly Balanced Bagging for Imbalanced Data. [Citation Graph (, )][DBLP]


  28. Link Propagation: A Fast Semi-supervised Learning Algorithm for Link Prediction. [Citation Graph (, )][DBLP]


  29. Side Effect Prediction Using Cooperative Pathways. [Citation Graph (, )][DBLP]


  30. Simultaneous inference of biological networks of multiple species from genome-wide data and evolutionary information: a semi-supervised approach. [Citation Graph (, )][DBLP]


  31. Protein complex prediction via verifying and reconstructing the topology of domain-domain interactions. [Citation Graph (, )][DBLP]


  32. Reaction graph kernels predict EC numbers of unknown enzymatic reactions in plant secondary metabolism. [Citation Graph (, )][DBLP]


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