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Yasubumi Sakakibara: [Publications] [Author Rank by year] [Co-authors] [Prefers] [Cites] [Cited by]

Publications of Author

  1. Yuji Takada, Kunihiko Hiraishi, Yasubumi Sakakibara
    Exact Learning of Semilinear Sets. [Citation Graph (0, 0)][DBLP]
    ALT, 1990, pp:314-324 [Conf]
  2. Yasubumi Sakakibara
    Occam Algorithms for Learning From Noisy Examples. [Citation Graph (0, 0)][DBLP]
    ALT, 1990, pp:193-208 [Conf]
  3. Yasubumi Sakakibara
    Grammatical Inference: An Old and New Paradigm. [Citation Graph (0, 0)][DBLP]
    ALT, 1995, pp:1-24 [Conf]
  4. Yasubumi Sakakibara, Klaus P. Jantke, Steffen Lange
    Learning Languages by Collecting Cases and Tuning Parameters. [Citation Graph (0, 0)][DBLP]
    AII/ALT, 1994, pp:532-546 [Conf]
  5. Yuji Kawada, Yasubumi Sakakibara
    Discriminative Detection of Cis-Acting Regulatory Variation From Location Data. [Citation Graph (0, 0)][DBLP]
    APBC, 2006, pp:89-98 [Conf]
  6. Satoshi Kobayashi, Takashi Yokomori, Yasubumi Sakakibara
    An Algorithm for Testing Structure Freeness of Biomolecular Sequences. [Citation Graph (0, 0)][DBLP]
    Aspects of Molecular Computing, 2004, pp:266-277 [Conf]
  7. Takashi Yokomori, Yasubumi Sakakibara, Satoshi Kobayashi
    A Magic Pot : Self-assembly Computation Revisited. [Citation Graph (0, 0)][DBLP]
    Formal and Natural Computing, 2002, pp:418-430 [Conf]
  8. Yasubumi Sakakibara
    Learning Context-Free Grammars from Structural Data in Polynomial Time. [Citation Graph (0, 0)][DBLP]
    COLT, 1988, pp:330-344 [Conf]
  9. Yasubumi Sakakibara, Rani Siromoney
    A Noise Model on Learning Sets of Strings. [Citation Graph (0, 0)][DBLP]
    COLT, 1992, pp:295-302 [Conf]
  10. Yasubumi Sakakibara, Michael Brown, Richard Hughey, I. Saira Mian, Kimmen Sjölander, Rebecca C. Underwood, David Haussler
    Recent Methods for RNA Modeling Using Stochastic Context-Free Grammars. [Citation Graph (0, 0)][DBLP]
    CPM, 1994, pp:289-306 [Conf]
  11. Yuji Kawada, Yasubumi Sakakibara
    Discriminative Discovery of Transcription Factor Binding Sites from Location Data. [Citation Graph (0, 0)][DBLP]
    CSB, 2005, pp:86-89 [Conf]
  12. Hiroshi Matsui, Kengo Sato, Yasubumi Sakakibara
    Pair Stochastic Tree Adjoining Grammars for Aligning and Predicting Pseudoknot RNA Structures. [Citation Graph (0, 0)][DBLP]
    CSB, 2004, pp:290-299 [Conf]
  13. Yasubumi Sakakibara
    Solving Computational Learning Problems of Boolean Formulae on DNA Computers. [Citation Graph (0, 0)][DBLP]
    DNA Computing, 2000, pp:220-230 [Conf]
  14. Yasubumi Sakakibara
    Population Computation and Majority Inference in Test Tube. [Citation Graph (0, 0)][DBLP]
    DNA, 2001, pp:82-91 [Conf]
  15. Yasubumi Sakakibara, Takahiro Hohsaka
    In Vitro Translation-Based Computations. [Citation Graph (0, 0)][DBLP]
    DNA, 2003, pp:197-202 [Conf]
  16. Yasubumi Sakakibara, Hiroshi Imai
    A DNA-based Computational Model Using a Specific Type of Restriction Enzyme. [Citation Graph (0, 0)][DBLP]
    DNA, 2002, pp:315-325 [Conf]
  17. Junna Kuramochi, Yasubumi Sakakibara
    Intensive In Vitro Experiments of Implementing and Executing Finite Automata in Test Tube. [Citation Graph (0, 0)][DBLP]
    DNA, 2005, pp:193-202 [Conf]
  18. Hirotaka Nakagawa, Kensaku Sakamoto, Yasubumi Sakakibara
    Development of an In Vivo Computer Based on Escherichia coli. [Citation Graph (0, 0)][DBLP]
    DNA, 2005, pp:203-212 [Conf]
  19. Kengo Sato, Yasubumi Sakakibara
    RNA secondary structural alignment with conditional random fields. [Citation Graph (0, 0)][DBLP]
    ECCB/JBI, 2005, pp:242- [Conf]
  20. Yasubumi Sakakibara
    An Efficient Learning of Context-Free Grammars for Bottom-Up Parsers. [Citation Graph (0, 0)][DBLP]
    FGCS, 1988, pp:447-454 [Conf]
  21. Yasubumi Sakakibara, Michael Brown, Rebecca C. Underwood, I. Saira Mian, David Haussler
    Stochastic Context-Free Grammars for Modeling RN. [Citation Graph (0, 0)][DBLP]
    HICSS (5), 1994, pp:284-294 [Conf]
  22. Mostefa Golea, Masahiro Matsuoka, Yasubumi Sakakibara
    Stochastic simple recurrent neural networks. [Citation Graph (0, 0)][DBLP]
    ICGI, 1996, pp:262-273 [Conf]
  23. Yasubumi Sakakibara, Hidenori Muramatsu
    Learning Context-Free Grammars from Partially Structured Examples. [Citation Graph (0, 0)][DBLP]
    ICGI, 2000, pp:229-240 [Conf]
  24. Yasubumi Sakakibara, Mitsuhiro Kondo
    GA-based Learning of Context-Free Grammars using Tabular Representations. [Citation Graph (0, 0)][DBLP]
    ICML, 1999, pp:354-360 [Conf]
  25. Yasubumi Sakakibara
    Pair hidden Markov models on tree structures. [Citation Graph (0, 0)][DBLP]
    ISMB (Supplement of Bioinformatics), 2003, pp:232-240 [Conf]
  26. Yasubumi Sakakibara
    Programming in Modal Logic: An Extension of PROLOG based on Modal Logic. [Citation Graph (0, 0)][DBLP]
    LP, 1986, pp:81-91 [Conf]
  27. Yasubumi Sakakibara, Kiyoshi Asai, Kengo Sato
    Stem Kernels for RNA Sequence Analyses. [Citation Graph (0, 0)][DBLP]
    BIRD, 2007, pp:278-291 [Conf]
  28. Hiroshi Matsui, Kengo Sato, Yasubumi Sakakibara
    Pair stochastic tree adjoining grammars for aligning and predicting pseudoknot RNA structures. [Citation Graph (0, 0)][DBLP]
    Bioinformatics, 2005, v:21, n:11, pp:2611-2617 [Journal]
  29. Yasubumi Sakakibara
    Development of a Bacteria Computer: From in silico Finite Automata to in virto AND in vivo. [Citation Graph (0, 0)][DBLP]
    Bulletin of the EATCS, 2005, v:87, n:, pp:165-178 [Journal]
  30. Yasubumi Sakakibara
    Efficient Learning of Context-Free Grammars from Positive Structural Examples [Citation Graph (0, 0)][DBLP]
    Inf. Comput., 1992, v:97, n:1, pp:23-60 [Journal]
  31. Yasubumi Sakakibara
    On Learning from Queries and Counterexamples in the Presence of Noise. [Citation Graph (0, 0)][DBLP]
    Inf. Process. Lett., 1991, v:37, n:5, pp:279-284 [Journal]
  32. Yasubumi Sakakibara, Hiroshi Imai
    A DNA-Based Computational Model Using a Specific Type of Restriction Enzymes. [Citation Graph (0, 0)][DBLP]
    Journal of Automata, Languages and Combinatorics, 2004, v:9, n:1, pp:111-119 [Journal]
  33. Yasubumi Sakakibara
    Noise-Tolerant Occam Algorithms and Their Applications to Learning Decision Trees. [Citation Graph (0, 0)][DBLP]
    Machine Learning, 1993, v:11, n:, pp:37-62 [Journal]
  34. Georgios Paliouras, Yasubumi Sakakibara
    Guest editorial to the special issue on grammatical inference. [Citation Graph (0, 0)][DBLP]
    Machine Learning, 2007, v:66, n:1, pp:3-5 [Journal]
  35. Yasubumi Sakakibara
    DNA-based algorithms for learning Boolean formulae. [Citation Graph (0, 0)][DBLP]
    Natural Computing, 2003, v:2, n:2, pp:153-171 [Journal]
  36. Christoph Globig, Klaus P. Jantke, Steffen Lange, Yasubumi Sakakibara
    On Case Based Learnability of Language. [Citation Graph (0, 0)][DBLP]
    New Generation Comput., 1997, v:15, n:1, pp:39-83 [Journal]
  37. Yasubumi Sakakibara
    Grammatical Inference in Bioinformatics. [Citation Graph (0, 0)][DBLP]
    IEEE Trans. Pattern Anal. Mach. Intell., 2005, v:27, n:7, pp:1051-1062 [Journal]
  38. Yasubumi Sakakibara
    Learning context-free grammars using tabular representations. [Citation Graph (0, 0)][DBLP]
    Pattern Recognition, 2005, v:38, n:9, pp:1372-1383 [Journal]
  39. Yasubumi Sakakibara, Satoshi Kobayashi
    Sticker systems with complex structures. [Citation Graph (0, 0)][DBLP]
    Soft Comput., 2001, v:5, n:2, pp:114-120 [Journal]
  40. Satoshi Kobayashi, Yasubumi Sakakibara
    Multiple splicing systems and the universal computability. [Citation Graph (0, 0)][DBLP]
    Theor. Comput. Sci., 2001, v:264, n:1, pp:3-23 [Journal]
  41. Yasubumi Sakakibara
    Learning Context-Free Grammars from Structural Data in Polynomial Time. [Citation Graph (0, 0)][DBLP]
    Theor. Comput. Sci., 1990, v:76, n:2-3, pp:223-242 [Journal]
  42. Yasubumi Sakakibara
    Recent Advances of Grammatical Inference. [Citation Graph (0, 0)][DBLP]
    Theor. Comput. Sci., 1997, v:185, n:1, pp:15-45 [Journal]
  43. Yasubumi Sakakibara, Claudio Ferretti
    Splicing on Tree-Like Structures. [Citation Graph (0, 0)][DBLP]
    Theor. Comput. Sci., 1999, v:210, n:2, pp:227-243 [Journal]

  44. Development of a Bacteria Computer: From in silico Finite Automata to in vitro and in vivo. [Citation Graph (, )][DBLP]


  45. Operon Structure Optimization by Random Self-assembly. [Citation Graph (, )][DBLP]


  46. A Non-parametric Bayesian Approach for Predicting RNA Secondary Structures. [Citation Graph (, )][DBLP]


  47. Statistical prediction of protein-chemical interactions based on chemical structure and mass spectrometry data. [Citation Graph (, )][DBLP]


  48. Accurate identification of orthologous segments among multiple genomes. [Citation Graph (, )][DBLP]


  49. Accurate identification of orthologous segments among multiple genomes. [Citation Graph (, )][DBLP]


  50. Directed acyclic graph kernels for structural RNA analysis. [Citation Graph (, )][DBLP]


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