8. ALT 1997: Sendai, Japan

@proceedings{DBLP:conf/alt/1997,
  editor    = {Ming Li and
               Akira Maruoka},
  title     = {Algorithmic Learning Theory, 8th International Conference, ALT
               '97, Sendai, Japan, October 6-8, 1997, Proceedings},
  booktitle = {ALT},
  publisher = {Springer},
  series    = {Lecture Notes in Computer Science},
  volume    = {1316},
  year      = {1997},
  isbn      = {3-540-63577-7},
  bibsource = {DBLP, http://dblp.uni-trier.de}
}

Session 1

• Manuel Blum: Program Error Detection/Correction: Turning PAC Learning into PERFECT Learning (Abstract). 1

Session 2

• Andris Ambainis, Kalvis Apsitis, Rusins Freivalds, William I. Gasarch, Carl H. Smith: Team Learning as a Game. 2-17
• Eiju Hirowatari, Setsuo Arikawa: Inferability of Recursive Real-Valued Functions. 18-31
• Sanjay Jain, Steffen Lange, Jochen Nessel: Learning of R.E. Languages from Good Examples. 32-47
• Satoshi Kobayashi, Takashi Yokomori: Identifiability of Subspaces and Homomorphic Images of Zero-Reversible Languages. 48-61

Session 3

• Leonard Pitt: On Exploiting Knowledge and Concept Use in Learning Theory. 62-84

Session 4

• Carlos Domingo, Tatsuie Tsukiji, Osamu Watanabe: Partial Occam's Razor and Its Applications. 85-99
• Meera Sitharam, Timothy Straney: Deranomized Learning of Boolean Functions. 100-115
• Rajesh Parekh, Vasant Honavar: Learning DFA from Simple Examples. 116-131

Session 5

• François Denis, Rémi Gilleron: PAC Learning under Helpful Distributions. 132-145
• Hongzhu Qiao, Nageswara S. V. Rao, Vladimir Protopopescu: PAC Learning Using Nadaraya-Watson Estimator Based on Orthonormal Systems. 146-160
• Endre Boros, Toshihide Ibaraki, Kazuhisa Makino: Monotone Extensions of Boolean Data Sets. 161-175

Session 6

• Masahiko Sato: Classical Brouwer-Heyting-Kolmogorov Interpretation. 176-196

Session 7

• Yasuhito Mukouchi: Inferring a System from Examples with Time Passage. 197-211
• Satoshi Matsumoto, Yukiko Hayashi, Takayoshi Shoudai: Polynomial Time Inductive Inference of Regular Term Tree Languages from Positive Data. 212-227
• John Case, Sanjay Jain, Arun Sharma: Synthesizing Noise-Tolerant Language Learners. 228-243
• Andris Ambainis, Kalvis Apsitis, Cristian Calude, Rusins Freivalds, Marek Karpinski, Tomas Larfeldt, Iveta Sala, Juris Smotrovs: Effects of Kolmogorov Complexity Present in Inductive Inference as Well. 244-259

Session 8

• Thomas Erlebach, Peter Rossmanith, Hans Stadtherr, Angelika Steger, Thomas Zeugmann: Learning One-Variable Pattern Languages Very Efficiently on Average, in Parallel, and by Asking Queries. 260-276
• Johannes Köbler, Wolfgang Lindner: Oracles in Sigma^p₂ are Sufficient for Exact Learning. 277-290
• Vikraman Arvind, N. V. Vinodchandran: Exact Learning via Teaching Assistants (Extended Abstract). 291-306
• Atsuyoshi Nakamura: An Efficient Exact Learning Algorithm for Ordered Binary Decision Diagrams. 307-322

Session 9

• V. G. Vovk: Probability Theory for the Brier Game. 323-338
• Xiaolong Zhang, Masayuki Numao: Learning and Revising Theories in Noisy Domains. 339-351
• Leonid Gurvits: A Note on a Scale-Sensitive Dimension of Linear Bounded Functionals in Banach Spaces. 352-363

Session 10

• Wolfgang Maass: On the Relevance of Time in Neural Computation and Learning. 364-384

Session 11

• Eiji Takimoto, Ken'ichi Hirai, Akira Maruoka: A Simple Algorithm for Predicting Nearly as Well as the Best Pruning Labeled with the Best Prediction Values of a Decision Tree. 385-400
• Stephen Kwek: Learning Disjunctions of Features. 401-415
• Hiroshi Sakamoto: Learning Simple Deterministic Finite-Memory Automata. 416-431

Session 12

• Hiroki Arimura: Learning Acyclic First-Order Horn Sentences from Entailment. 432-445
• Tibor Hegedüs, Piotr Indyk: On Learning Disjunctions of Zero-One Treshold Functions with Queries. 446-460