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ORCID4. AII/ 5. ALT 1994: Reinhardsbrunn Castle, Germany
- Setsuo Arikawa, Klaus P. Jantke:
Algorithmic Learning Theory, 4th International Workshop on Analogical and Inductive Inference, AII '94, 5th International Workshop on Algorithmic Learning Theory, ALT '94, Reinhardsbrunn Castle, Germany, October 10-15, 1994, Proceedings. Lecture Notes in Computer Science 872, Springer 1994, ISBN 3-540-58520-6
Analogical and Inductive Inference
Invited Talks
- Janis Barzdins:
Towards Efficient Inductive Synthesis from Input/Output Examples (Abstract). 1 - Wolfgang Bibel, Michael Thielscher:
Deductive Plan Generation. 2-5 - Nachum Dershowitz:
From Specifications to Programs: Induction in the Service of Synthesis (Abstract). 6-7 - Thomas Zeugmann:
Average Case Analysis of Pattern Language Learning Algorithms (Abstract). 8-9
Selected Papers
- Andris Ambainis, Juris Smotrovs
:
Enumerable Classes of Total Recursive Functions: Complexity of Inductive Inference. 10-25 - Kalvis Apsitis:
Derived Sets and Inductive Inference. 26-39 - Oksana Arnold, Klaus P. Jantke:
Therapy Plan Generation as Program Synthesis. 40-55 - Shuo Bai:
A Calculus for Logical Clustering. 56-63 - Ganesh Baliga, John Case:
Learning with Higher Order Additional Information. 64-75 - Alvis Brazma, Karlis Cerans:
Efficient Learning of Regular Expressions from Good Examples. 76-90 - Rusins Freivalds, Ognian Botuscharov, Rolf Wiehagen:
Identifying Nearly Minimal Gödel Numbers From Additional Information. 91-99 - Rusins Freivalds, Dace Gobleja, Marek Karpinski, Carl H. Smith:
Co-learnability and FIN-identifiability of Enumerable Classes of Total Recursive Functions. 100-105 - Christoph Globig, Steffen Lange:
On Case-Based Representability and Learnability of Languages. 106-120 - Kouichi Hirata:
Rule-Generating Abduction for Recursive Prolog. 121-136 - Toshiharu Iwatani, Shun'ichi Tano, Atsushi Inoue, Wataru Okamoto:
Fuzzy Analogy Based Reasoning and Classification of Fuzzy Analogies. 137-148 - Yasuyuki Koga, Eiju Hirowatari, Setsuo Arikawa:
Explanation-Based Reuse of Prolog Programs. 149-160 - Chowdhury Rahman Mofizur, Masayuki Numao:
Constructive Induction for Recursive Programs. 161-175 - Hsieh-Chang Tu, Carl H. Smith:
Training Diagraphs. 176-186
Algorithmic Learning Theory
Invited Talks
- Naoki Abe:
Towards Realistic Theories of Learning. 187-209 - Michael M. Richter:
A Unified Approach to Inductive Logic and Case-Based Reasoning (Extended Abstract). 210 - Carl H. Smith:
Three Decades of Team Learning. 211-228
Selected Papers
- Peter Auer, Nicolò Cesa-Bianchi:
On-line Learning with Malicious Noise and the Closure Algorithm. 229-247 - Shai Ben-David, Eli Dichterman:
Learnability with Restricted Focus of Attention guarantees Noise-Tolerance. 248-259 - Alvis Brazma:
Efficient Algorithm for Learning Simple Regular Expressions from Noisy Examples. 260-271 - Zhixiang Chen:
A Note on Learning DNF Formulas Using Equivalence and Incomplete Membership Queries. 272-281 - Claudio Ferretti, Giancarlo Mauri:
Identifying Regular Languages over Partially-Commutative Monoids. 282-289 - William I. Gasarch, Mark G. Pleszkoch, Mahendran Velauthapillai:
Classification Using Information. 290-300 - Akira Ishino, Akihiro Yamamoto:
Learning from Examples with Typed Equational Programming. 301-316 - Hiroki Ishizaka, Hiroki Arimura, Takeshi Shinohara:
Finding Tree Patterns Consistent with Positive and Negative Examples Using Queries. 317-332 - Sanjay Jain:
Program Synthesis in the Presence of Infinite Number of Inaccuracies. 333-348 - Sanjay Jain, Arun Sharma:
On Monotonic Strategies for Learning r.e. Languages. 349-364 - Shyam Kapur:
Language Learning under Various Types of Constraint Combinations. 365-378 - Shigetomo Kimura, Atsushi Togashi, Norio Shiratori:
Synthesis Algorithm for Recursive Process by µ-calculus (Extended Abstract). 379-394 - Efim B. Kinber:
Monotonicity versus Efficiency for Learning Languages from Texts. 395-406 - Satoshi Kobayashi, Takashi Yokomori:
Learning Concatenations of Locally Testable Languages from Positive Data. 407-422 - Steffen Lange, Jochen Nessel, Rolf Wiehagen:
Language Learning from Good Examples. 423-437 - Steffen Lange, Phil Watson:
Machine Discovery in the Presence of Incomplete or Ambiguous Data. 438-452 - Steffen Lange, Thomas Zeugmann:
Set-Driven and Rearrangement-Independent Learning of Recursive Languages. 453-468 - Satoshi Matsumoto, Ayumi Shinohara:
Refutably Probably Approximately Correct Learning. 469-483 - Yasuhito Mukouchi:
Inductive Inference of an Approximate Concept from Positive Data. 484-499 - Atsuyoshi Nakamura, Naoki Abe, Jun'ichi Takeuchi:
Efficient Distribution-free Population Learning of Simple Concepts. 500-515 - Yoshiaki Okubo, Makoto Haraguchi:
Constructing Predicate Mappings for Goal-Dependent Abstraction. 516-531 - Yasubumi Sakakibara, Klaus P. Jantke, Steffen Lange:
Learning Languages by Collecting Cases and Tuning Parameters. 532-546 - Eiji Takimoto, Ichiro Tajika, Akira Maruoka:
Mutual Information Gaining Algorithm and Its Relation to PAC-Learning Algorithm. 547-559 - Noriyuki Tanida, Takashi Yokomori:
Inductive Inference of Monogenic Pure Context-free Languages. 560-573
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