Téléchargez le livre : Algorithmic Learning in a Random World
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Description
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.
Pages
324 pages
Collection
n.c
Parution
2005-12-05
Marque
Springer
EAN papier
9780387001524
EAN PDF
9780387250618
Informations sur l'ebook
Nombre pages copiables
3
Nombre pages imprimables
32
Taille du fichier
15708 Ko
Ce livre est accessible aux handicaps Voir les informations sur l'accessibilité
Prix
168,79 €

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