Computational Learning Theory: EuroCOLT '93

John Shawe-Taylor; Martin Anthony (eds.)

ISBN 10: 0198534922 / ISBN 13: 9780198534921

Edité par Oxford: Clarendon, 1994, 1994

A propos de cet article

New! Hard bound in rust cloth with bright silver titles, first edition, xi + Pp239. A new, unread copy without a dust jacket. 560 grams. A volume from The Institute of Mathematics & Its Applications Conference Series; New Series, Number 53.

N° de réf. du vendeur 5421

Détails bibliographiques

• Titre: Computational Learning Theory: EuroCOLT '93
• Éditeur: Oxford: Clarendon, 1994
• Date de parution: 1994
• Langue: anglais
• ISBN à 10 chiffres: 0198534922
• ISBN à 13 chiffres: 9780198534921
• Reliure: Couverture rigide
• Édition: Edition originale
• État de la jaquette: Jaquette
• Catalogues du vendeur:
  • Mathematics, Statistics

A propos de ce titre

Synopsis

This volume contains 17 of the contributed papers presented at the 1st European Conference on Computational Learning Theory. Also included are invited presentations on the complexity of learning on neural nets, on new directions in computational learning theory, and on a neurodial model for cognitive functions. The proceedings give an overview of current work in computational learning theory, ranging from results inspired by neural network research to those arising from more classical artificial intelligence approaches. The study of machine learning within the mathematical framework of complexity theory has been a relatively recent development. The burgeoning interest in the application of machine learning to a wide variety of problems from control to financial market prediction has fired a corresponding upsurge in mathematical research.

Présentation de l'éditeur

Computational learning theory is a subject which has been advancing rapidly in the last few years. The authors concentrate on the probably approximately correct model of learning, and gradually develop the ideas of efficiency considerations. Finally, applications of the theory to artificial neural networks are considered. Many exercises are included throughout, and the list of references is extensive. This volume is relatively self contained as the necessary background material from logic, probability and complexity theory is included. It will therefore form an introduction to the theory of computational learning, suitable for a broad spectrum of graduate students from theoretical computer science and mathematics.