Systems That Learn presents a mathematical framework for the study of learning in a variety of domains. It provides the basic concepts and techniques of learning theory as well as a comprehensive account of what is currently known about a variety of learning paradigms.Daniel N. Osherson and Scott We
Systems That Learn: An Introduction to Learning Theory
โ Scribed by Jain S., Osherson D., Royer J.S., Sharma A.
- Publisher
- MIT
- Year
- 1999
- Tongue
- English
- Series
- Learning, Development, and Conceptual Change
- Edition
- 2ed
- Category
- Library
No coin nor oath required. For personal study only.
โฆ Synopsis
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.
๐ SIMILAR VOLUMES
<P>Systems That Learn presents a mathematical framework for the study of learning in a variety of domains. It provides the basic concepts and techniques of learning theory as well as a comprehensive account of what is currently known about a variety of learning paradigms.Daniel N. Osherson and Scott
Emphasizing issues of computational efficiency, Michael Kearns and Umesh Vazirani introduce a number of central topics in computational learning theory for researchers and students in artificial intelligence, neural networks, theoretical computer sc
Emphasizing issues of computational efficiency, Michael Kearns and Umesh Vazirani introduce a number of central topics in computational learning theory for researchers and students in artificial intelligence, neural networks, theoretical computer science, and statistics.Computational learning theory
<P>Emphasizing issues of computational efficiency, Michael Kearns and Umesh Vazirani introduce a number of central topics in computational learning theory for researchers and students in artificial intelligence, neural networks, theoretical computer science, and statistics.Computational learning the