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ORCID3. ALT 1992: Tokyo, Japan
- Shuji Doshita, Koichi Furukawa, Klaus P. Jantke, Toyoaki Nishida:
Algorithmic Learning Theory, Third Workshop, ALT '92, Tokyo, Japan, October 20-22, 1992, Proceedings. Lecture Notes in Computer Science 743, Springer 1993, ISBN 3-540-57369-0
Invited Papers
- Setsuko Otsuki:
Discovery Learning in Intelligent Tutoring Systems. 3-12 - Rolf Wiehagen:
From Inductive Inference to Algorithmic Learning Theory. 13-24 - Akihiko Konagaya:
A Stochastic Approach to Genetic Information Processing. 25-36
Learning via Query
- Takashi Yokomori:
On Learning Systolic Languages. 41-52 - José L. Balcázar, Josep Díaz, Ricard Gavaldà
, Osamu Watanabe:
A Note on the Query Complexity of Learning DFA (Extended Abstract). 53-62 - Kimihito Ito, Akihiro Yamamoto:
Polynomial-Time MAT Learning of Multilinear Logic Programs. 63-74
Neural Networks
- Takio Kurita:
Iterative Weighted Least Squares Algorithms for Neural Networks Classifiers. 77-86 - Koichi Niijima:
Domains of Attraction in Autoassociative Memory Networks for Character Pattern Recognition. 87-98 - Shotaro Akaho:
Regularization Learning of Neural Networks for Generalization. 99-110 - Ryotaro Kamimura:
Competitive Learning by Entropy Minimization. 111-122
Inductive Inference
- Yasuhito Mukouchi:
Inductive Inference with Bounded Mind Changes. 125-134 - Hiroki Ishizaka, Hiroki Arimura, Takeshi Shinohara:
Efficient Inductive Inference of Primitive Prologs from Positive Data. 135-146 - Shyam Kapur:
Monotonic Language Learning. 147-158 - Sanjay Jain, Arun Sharma:
Prudence in Vacillatory Language Identification (Extended Abstract). 159-168
Analogical Reasoning
- Kazuhiro Ueda, Saburo Nagano:
Implementation of Heuristic Problem Solving Process Including Analogical Reasoning. 171-182 - Yoshiaki Okubo, Makoto Haraguchi:
Planning with Abstraction Based on Partial Predicate Mappings. 183-194
Approximate Learning
- Yoshifumi Sakai, Akira Maruoka:
Learning k-Term Monotone Boolean Formulae. 197-207 - Jun'ichi Takeuchi:
Some Improved Sample Complexity Bounds in the Probabilistic PAC Learning Model. 208-219 - Masahiro Matsuoka:
An Application Of Bernstein Polynomials in PAC Model. 220-228 - Tatsuya Akutsu, Atsuhiro Takasu:
On PAC Learnability of Functional Dependencies. 229-239 - Hiroshi Mamitsuka, Kenji Yamanishi:
Protein Secondary Structure Prediction Based on Stochastic-Rule Learning. 240-251 - Ken-ichiro Kakihara, Hiroshi Imai:
Notes on the PAC Learning of Geometric Concepts with Additional Information. 252-259
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