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4. ALT 1993: Tokyo, Japan
- Klaus P. Jantke
, Shigenobu Kobayashi, Etsuji Tomita, Takashi Yokomori:
Algorithmic Learning Theory, 4th International Workshop, ALT '93, Tokyo, Japan, November 8-10, 1993, Proceedings. Lecture Notes in Computer Science 744, Springer 1993, ISBN 3-540-57370-4
Invited Papers
- Philip Laird:
Identifying and Using Patterns in Sequential Data. 1-18 - Satoru Miyano:
Learning Theory Toward Genome Informatics. 19-36 - Stephen H. Muggleton:
Optimal Layered Learning: A PAC Approach to Incremental Sampling. 37-44
Inductive Logic and Inference
- Jun Arima, Hajime Sawamura:
Reformulation of Explanation by Linear Logic: Toward Logic for Explanation. 45-57 - Janis Barzdins, Guntis Barzdins, Kalvis Apsitis, Ugis Sarkans:
Towards Efficient Inductive Synthesis of Expressions from Input/Output Examples. 59-72 - Masami Hagiya:
A Typed Lambda-Calculus for Proving-by-Example and Bottom-Up Generalization Procedure. 73-86 - Klaus P. Jantke, Steffen Lange:
Case-Based Representation and Learning of Pattern Languages. 87-100 - Taisuke Sato
Inductive Resolution. 101-110 - Akihiro Yamamoto:
Generalized Unification as Background Knowledge in Learning Logic Programs. 111-122
Inductive Inference
- Yasuhito Mukouchi, Setsuo Arikawa:
Inductive Inference Machines That Can Refute Hypothesis Spaces. 123-136 - Rusins Freivalds, Carl H. Smith:
On the Duality Between Mechanistic Learners and What it is They Learn. 137-149 - Sanjay Jain, Arun Sharma:
On Aggregating Teams of Learning Machines. 150-163 - Juris Viksna:
Learning With Growing Quality. 164-172 - Robert P. Daley, Bala Kalyanasundaram:
Use of Reduction Arguments in Determining Popperian FIN-Type Learning Capabilities. 173-186 - Takashi Moriyama, Masako Sato:
Properties of Language Classes With Finite Elasticity. 187-196 - Shyam Kapur:
Uniform Charakterizations of Various Kinds of Language Learning. 197-208 - Timo Knuutila:
How to Invent Characterizable Inference Methods for Regular Languages. 209-222
Approximate Learning
- Jorge Ricardo Cuellar, Hans Ulrich Simon
Neural Discriminant Analysis. 223-236 - Makoto Iwayama, Nitin Indurkhya, Hiroshi Motoda:
A New Algorithm for Automatic Configuration of Hidden Markov Models. 237-250 - Akito Sakurai:
On the VC-Dimension of Depth Four Threshold Circuits and the Complexity of Boolean-Valued Functions. 251-264 - Eiji Takimoto, Akira Maruoka:
On the Sample Complexity of Consistent Learning with One-Sided Error. 265-278 - Ayumi Shinohara
Complexity of Computing Vapnik-Chervonenkis Dimension. 279-287 - Susumu Hasegawa, Hiroshi Imai, Masaki Ishiguro:
Epsilon-Approximations of k-label Spaces. 288-299
Query Learning
- Atsuyoshi Nakamura, Naoki Abe:
Exact Learning of Linear Combinations of Monotone Terms from Function Value Queries. 300-313 - Rani Siromoney, D. Gnanaraj Thomas, K. G. Subramanian, V. Rajkumar Dare:
Thue Systems and DNA - A Learning Algorithm for a Subclass. 314-327 - Yoshiyasu Ishigami, Sei'ichi Tani:
The VC-Dimensions of Finite Automata with n States. 328-341
Explanation-Based Learning
- Kenichi Yoshida, Hiroshi Motoda, Nitin Indurkhya:
Unifying Learning Methods by Colored Digraphs. 342-355 - Masaki Suwa, Hiroshi Motoda:
A Perceptual Criterion for Visually Controlling Learning. 356-369 - Satoshi Kobayashi:
Learning Strategies Using Decision Lists. 370-383
New Learning Paradigms
- Ning Zhong, Setsuo Ohsuga:
A Decomposition Based Induction Model for Discovering Concept Clusters from Databases. 384-397 - Jean-Gabriel Ganascia:
Algebraic Structure of Some Learning Systems. 398-409 - Shusaku Tsumoto, Hiroshi Tanaka:
Induction of Probabilistic Rules Based on Rough Set Theory. 410-423
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