Analogical and Inductive Inference
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Invited Papers
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J. Barzdins | |
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Towards Efficient Inductive Synthesis from Input/Output Examples
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W. Bibel and M. Thielscher | |
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Deductive Plan Generation
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N. Dershowitz | |
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From Specifications to Programs: Induction in the Service of Synthesis
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T. Zeugmann | |
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Average Case Analysis of Pattern Language Learning Algorithms
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Selected Papers
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A. Ambainis and J. Smotrovs | |
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Enumerable Classes of Total Recursive Functions: Complexity of
Inductive Inference
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K. Apsitis | |
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Derived Sets and Inductive Inference
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O. Arnold and K.P. Jantke | |
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Therapy Plan Generation as Program Synthesis
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S. Bai | |
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A Calculus for Logical Clustering
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G. Baliga and J. Case | |
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Learning with Higher Order Additional Information
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A. Brazma and K. Cerans | |
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Efficient Learning of Regular Expressions from Good Examples
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R. Freivalds, O. Botuscharov and R. Wiehagen | |
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Identifying Nearly Minimal Gödel Numbers from Additional
Information
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R. Freivalds, D. Gobleja, M. Karpinski and C.H. Smith | |
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Co-Learnability and FIN-Identifiability of Enumerable Classes of Total
Recursive Functions
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C. Globig and S. Lange | |
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On Case-Based Representability and Learnability of Languages
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K. Hirata | |
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Rule-Generating Abduction for Recursive Prolog
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T. Iwatani, S. Tano, A. Inoue and W. Okamoto | |
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Fuzzy Analogy Based Reasoning and Classification of Fuzzy Analogies
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Y. Koga, E. Hirowatari and S. Arikawa | |
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Explanation-Based Reuse of Prolog Programs
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C.R. Mofizur and M. Numao | |
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Constructive Induction for Recursive Programs
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H.-C. Tu and C.H. Smith | |
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Training Digraphs
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Algorithmic Learning Theory
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Invited Talks
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N. Abe | |
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Towards Realistic Theories of Learning
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M.M. Richter | |
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A Unified Approach lo Inductive Logic and Case-Based Reasoning
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C.H. Smith | |
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Three Decades of Team Learning
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Selected Papers
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P. Auer and N. Cesa-Bianchi | |
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On-line Learning with Malicious Noise and the Closure Algorithm
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S. Ben-David and E. Dichterman | |
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Learnability with Restricted Focus of Attention Guarantees
Noise-Tolerance
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A. Brazma | |
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Efficient Algorithm for Learning Simple Regular Expressions from Noisy
Examples
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Z. Chen | |
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A Note on Learning DNF Formulas Using Equivalence and Incomplete
Membership Queries
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C. Ferretti and G. Mauri | |
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Identifying Regular Languages over Partially-Commutative Monoids
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W.I. Gasarch, M.G. Pleszkoch and M. Velauthapillai | |
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Classification Using Information
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A. Ishino and A. Yamamoto | |
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Learning from Examples with Typed Equational Programming
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H. Ishizaka, H. Arimura and T. Shinohara | |
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Finding Tree Patterns Consistent with Positive and Negative Examples
Using Queries
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S. Jain | |
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Program Synthesis in the Presence of Infinite Number of Inaccuracies
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S. Jain and A. Sharma | |
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On Monotonic Strategies for Learning r.e. Languages
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S. Kapur | |
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Language Learning under Various Types of Constraint Combinations
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S. Kimura, A. Togashi and N. Shiratori | |
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Synthesis Algorithm for Recursive Processes by mu-Calculus
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E. Kinber | |
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Monotonicity versus Efficiency for Learning Languages from Texts
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S. Kobayashi and T. Yokomori | |
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Learning Concatenations of Locally Testable Languages from Positive
Data
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S. Lange, J. Nessel and R. Wiehagen | |
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Language Learning from Good Examples
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S. Lange and P. Watson | |
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Machine Discovery in the Presence of Incomplete or Ambiguous Data
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S. Lange and T. Zeugmann | |
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Set-Driven and Rearrangement-Independent Learning of Recursive
Languages
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S. Matsumoto and A. Shinohara | |
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Refutably Probably Approximately Correct Learning
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Y. Mukouchi | |
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Inductive Inference of an Approximate Concept from Positive Data
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A. Nakamura, N. Abe and J. Takeuchi | |
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Efficient Distribution-Free Population Learning of Simple Concepts
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Y. Okubo and M. Haraguchi | |
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Constructing Predicate Mappings for Goal-Dependent Abstraction
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Y. Sakakibara, K.P. Jantke and S. Lange | |
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Learning Languages by Collecting Cases and Tuning Parameters
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E. Takimoto, I. Tajika and A. Maruoka | |
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Mutual Information Gaining Algorithm and Its Relation to
PAC-Learning Algorithm
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N. Tanida and T. Yokomori | |
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Inductive Inference of Monogenic Pure Context-Free Languages
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Index of Authors | | 575
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