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Formal learning theory is one of several mathematical approaches to the study of intelligent adaptation to the environment. The analysis developed in this book is based on a number theoretical approach to learning and uses the tools of recursive-function theory to understand how learners come to an accurate view of reality. This revised and expanded edition of a successful text provides a comprehensive, self-contained introduction to the concepts and techniques of the theory.
Exercises throughout the text provide experience in the use of computational arguments to prove facts about learning.
|I||Fundamentals of Learning Theory||1|
|4||Identification by Computable Scientists||61|
|II||Fundamental Paradigms Generalized||89|
|5||Strategies for Learning||91|
|6||Criteria of Learning||127|
|7||Inference of Approximations||151|
|III||Part III: Additional Topics||195|
|9||Team and Probabilistic Learning||197|
|10||Learning with Additional Information||221|
|11||Learning with Oracles||251|
|12||Complexity Issues in Identification||261|
|13||Beyond Identification by Enumeration||281|
|Author and Subject Index||309|