Systems That Learn: An Introduction to Learning Theory for Cognitive and Computer Scientists

Systems That Learn: An Introduction to L

📁 Systems That Learn: An Introduction to Learning Theory for Cognitive and Computer Scientists

✍ Daniel N. Osherson, Michael Stob, Scott Weinstein 📂 Library 📅 1986 🏛 The MIT Press 🌐 English

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 L

📁 Systems That Learn: An Introduction to Learning Theory

✍ Jain S., Osherson D., Royer J.S., Sharma A. 📂 Library 📅 1999 🏛 MIT 🌐 English

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-fun

An introduction to computational learnin

📁 An introduction to computational learning theory

✍ Michael J. Kearns, Umesh Virkumar Vazirani 📂 Library 📅 1994 🏛 MIT Press 🌐 English

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

An Introduction to Computational Learnin

📁 An Introduction to Computational Learning Theory

✍ Michael J. Kearns, Umesh Vazirani 📂 Library 📅 1994 🏛 The MIT Press 🌐 English

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

An introduction to computational learnin

📁 An introduction to computational learning theory

✍ Kearns M.J., Vazirani U.V. 📂 Library 📅 1994 🏛 MIT 🌐 English

<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

Computational learning theory and natura

📁 Computational learning theory and natural learning systems - Making Learning Systems Practical

✍ edited by Russell Greiner, Thomas Petsche and Stephen José Hanson. 📂 Library 📅 1997 🏛 MIT Press. 🌐 English

This is the fourth and final volume of papers from a series of workshops called "Computational Learning Theory and 'Natural' Learning Systems." The purpose of the workshops was to explore the emerging intersection of theoretical learning research and natural learning systems. The workshops drew rese

Systems That Learn: An Introduction to Learning Theory for Cognitive and Computer Scientists

✦ Synopsis