4. ALT 1993: Tokyo, Japan

Invited Papers

• Philip Laird:
  Identifying and Using Patterns in Sequential Data. 1-18
  
• Satoru Miyano:
  Learning Theory Toward Genome Informatics. 19-36
  
• Stephen 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, Sumitaka Akiba:
  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. G. 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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