Algorithmic Learning in a Random World

Vladimir Vovk

ISBN 10: 0387001522 / ISBN 13: 9780387001524

Edité par Springer, Berlin, Springer US, Springer, 2005

A propos de cet article

Neuware - Algorithmic Learning in a Random World describes recent theoretical and experimental developments in building computable approximations to Kolmogorov's algorithmic notion of randomness. Based on these approximations, a new set of machine learning algorithms have been developed that can be used to make predictions and to estimate their confidence and credibility in high-dimensional spaces under the usual assumption that the data are independent and identically distributed (assumption of randomness). Another aim of this unique monograph is to outline some limits of predictions: The approach based on algorithmic theory of randomness allows for the proof of impossibility of prediction in certain situations. The book describes how several important machine learning problems, such as density estimation in high-dimensional spaces, cannot be solved if the only assumption is randomness.

N° de réf. du vendeur 9780387001524

Détails bibliographiques

• Titre: Algorithmic Learning in a Random World
• Éditeur: Springer, Berlin, Springer US, Springer
• Date de parution: 2005
• Langue: anglais
• ISBN à 10 chiffres: 0387001522
• ISBN à 13 chiffres: 9780387001524
• Reliure: Buch
• État de l'article: Neu

A propos de ce titre

Synopsis

A new scientific monograph developing significant new algorithmic foundations in machine learning theory. Researchers and postgraduates in CS, statistics, and A.I. will find the book an authoritative and formal presentation of some of the most promising theoretical developments in machine learning.