J'achète
168,79 €
Téléchargement | DRM LCP 🛈 DRM Adobe 🛈
Lecture en ligne (streaming)
Ajouter à ma liste d'envies
Gagnez  25,32 €  en recommandant ce livre avec Readzis

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.

Détails
Auteur
Alex  Gammerman , Glenn  Shafer , Vladimir  Vovk
Marque
Springer
Collection
n.c
Parution
2005-12-05
Pages
324 pages
EAN papier
9780387001524
Langue
Anglais
Informations ebook
EAN PDF
9780387250618
Prix
168,79 €
En savoir plus
Nb pages copiables 3
Nb pages imprimables 32
Taille du fichier 15708 Ko
Ce livre est accessible aux handicaps   Informations sur l'accessibilité
Compatibilité

mobile-and-tablet Pour vérifier la compatibilité avec vos appareils,
consultez la page d'aide

Avis clients

Suggestions personnalisées

Restez informé(e) des événements et promotions ebook

Paiements sécurisés

Paiements sécurisés