Algorithmic learning theory : 4th International Workshop on Analogical and Inductive Inference, AII '94 [and] 5th International Workshop on Algorithmic Learning Theory, ALT '94, Reinhardsbrunn Castle, Germany, October 10-15, 1994 : proceedings
Author(s)
Bibliographic Information
Algorithmic learning theory : 4th International Workshop on Analogical and Inductive Inference, AII '94 [and] 5th International Workshop on Algorithmic Learning Theory, ALT '94, Reinhardsbrunn Castle, Germany, October 10-15, 1994 : proceedings
（Lecture notes in computer science, 872 . Lecture notes in artificial intelligence）
Springer-Verlag, c1994
- : gw
- : us
Available at 58 libraries
Note
Includes bibliographical references and index
Description and Table of Contents
Description
This volume presents the proceedings of the Fourth International Workshop on Analogical and Inductive Inference (AII '94) and the Fifth International Workshop on Algorithmic Learning Theory (ALT '94), held jointly at Reinhardsbrunn Castle, Germany in October 1994. (In future the AII and ALT workshops will be amalgamated and held under the single title of Algorithmic Learning Theory.)
The book contains revised versions of 45 papers on all current aspects of computational learning theory; in particular, algorithmic learning, machine learning, analogical inference, inductive logic, case-based reasoning, and formal language learning are addressed.
Table of Contents
Towards efficient inductive synthesis from input/output examples.- Deductive plan generation.- From specifications to programs: Induction in the service of synthesis.- Average case analysis of pattern language learning algorithms.- Enumerable classes of total recursive functions: Complexity of inductive inference.- Derived sets and inductive inference.- Therapy plan generation as program synthesis.- A calculus for logical clustering.- Learning with higher order additional information.- Efficient learning of regular expressions from good examples.- Identifying nearly minimal Goedel numbers from additional information.- Co-learnability and FIN-identifiability of enumerable classes of total recursive functions.- On case-based represent ability and learnability of languages.- Rule-generating abduction for recursive prolog.- Fuzzy analogy based reasoning and classification of fuzzy analogies.- Explanation-based reuse of prolog programs.- Constructive induction for recursive programs.- Training digraphs.- Towards realistic theories of learning.- A unified approach to inductive logic and case-based reasoning.- Three decades of team learning.- On-line learning with malicious noise and the closure algorithm.- Learnability with restricted focus of attention guarantees noise-tolerance.- Efficient algorithm for learning simple regular expressions from noisy examples.- A note on learning DNF formulas using equivalence and incomplete membership queries.- Identifying regular languages over partially-commutative monoids.- Classification using information.- Learning from examples with typed equational programming.- Finding tree patterns consistent with positive and negative examples using queries.- Program synthesis in the presence of infinite number of inaccuracies.- On monotonic strategies for learning r.e. languages.- Language learning under various types of constraint combinations.- Synthesis algorithm for recursive processes by ?-calculus.- Monotonicity versus efficiency for learning languages from texts.- Learning concatenations of locally testable languages from positive data.- Language learning from good examples.- Machine discovery in the presence of incomplete or ambiguous data.- Set-driven and rearrangement-independent learning of recursive languages.- Refutably probably approximately correct learning.- Inductive inference of an approximate concept from positive data.- Efficient distribution-free population learning of simple concepts.- Constructing predicate mappings for Goal-Dependent Abstraction.- Learning languages by collecting cases and tuning parameters.- Mutual information gaining algorithm and its relation to PAC-learning algorithm.- Inductive inference of monogenic pure context-free languages.
by "Nielsen BookData"