Algorithmic Learning in a Random World describes recent theoretical and experimental developments in building computable approximations to Kolmogorov's algorithmic notion of randomness. B… Plus…
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. TOC:Preface.- List of Principal results.- Introduction.- Conformal prediction.- Classification with conformal predictors.-Modifications of conformal predictors.- Probabilistic prediction I: impossibility results.- Probabilistic prediction II: Venn predictors.- Beyond exchangeability.- On-line compression modeling I: conformal prediction.- On-line compression modeling II: Venn prediction.- Perspectives and contrasts.- Appendix A: Probability theory.- Appendix B: Data sets.- Appendix C: FAQ.- Notation.- References.- Index. eBooks > Fremdsprachige eBooks > Englische eBooks > Sach- & Fachthemen > Mathematik; eBooks > Fremdsprachige eBooks > Englische eBooks > Sach- & Fachthemen > Informatik; eBooks > Fachbücher > Informatik; eBooks > Fachbücher > Mathematik , Springer, PDF, Springer<
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Algorithmic Learning in a Random World describes recent theoretical and experimental developments in building computable approximations to Kolmogorov's algorithmic notion of randomness. B… Plus…
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. TOC:Preface.- List of Principal results.- Introduction.- Conformal prediction.- Classification with conformal predictors.-Modifications of conformal predictors.- Probabilistic prediction I: impossibility results.- Probabilistic prediction II: Venn predictors.- Beyond exchangeability.- On-line compression modeling I: conformal prediction.- On-line compression modeling II: Venn prediction.- Perspectives and contrasts.- Appendix A: Probability theory.- Appendix B: Data sets.- Appendix C: FAQ.- Notation.- References.- Index. eBook Alexander Gammerman#Glenn Shafer#Vladimir Vovk 05.12.2005, Springer, Springer<
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Algorithmic Learning in a Random World describes recent theoretical and experimental developments in building computable approximations to Kolmogorov's algorithmic notion of randomness. B… Plus…
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.; PDF; Computing > Computer programming / software development > Algorithms & data structures, Springer US<
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Algorithmic Learning in a Random World describes recent theoretical and experimental developments in building computable approximations to Kolmogorov's algorithmic notion of randomness. B… Plus…
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., Springer<
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Algorithmic Learning in a Random World describes recent theoretical and experimental developments in building computable approximations to Kolmogorov's algorithmic notion of randomness. B… Plus…
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. TOC:Preface.- List of Principal results.- Introduction.- Conformal prediction.- Classification with conformal predictors.-Modifications of conformal predictors.- Probabilistic prediction I: impossibility results.- Probabilistic prediction II: Venn predictors.- Beyond exchangeability.- On-line compression modeling I: conformal prediction.- On-line compression modeling II: Venn prediction.- Perspectives and contrasts.- Appendix A: Probability theory.- Appendix B: Data sets.- Appendix C: FAQ.- Notation.- References.- Index. eBooks > Fremdsprachige eBooks > Englische eBooks > Sach- & Fachthemen > Mathematik; eBooks > Fremdsprachige eBooks > Englische eBooks > Sach- & Fachthemen > Informatik; eBooks > Fachbücher > Informatik; eBooks > Fachbücher > Mathematik , Springer, PDF, Springer<
Nr. A1031477095. Frais d'envoiLieferzeiten außerhalb der Schweiz 3 bis 21 Werktage, , Sofort per Download lieferbar, zzgl. Versandkosten. (EUR 17.87)
Algorithmic Learning in a Random World describes recent theoretical and experimental developments in building computable approximations to Kolmogorov's algorithmic notion of randomness. B… Plus…
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. TOC:Preface.- List of Principal results.- Introduction.- Conformal prediction.- Classification with conformal predictors.-Modifications of conformal predictors.- Probabilistic prediction I: impossibility results.- Probabilistic prediction II: Venn predictors.- Beyond exchangeability.- On-line compression modeling I: conformal prediction.- On-line compression modeling II: Venn prediction.- Perspectives and contrasts.- Appendix A: Probability theory.- Appendix B: Data sets.- Appendix C: FAQ.- Notation.- References.- Index. eBook Alexander Gammerman#Glenn Shafer#Vladimir Vovk 05.12.2005, Springer, Springer<
Nr. 24482173. Frais d'envoi, Sofort per Download lieferbar, zzgl. Versandkosten, Lieferzeiten außerhalb der Schweiz 3 bis 21 Werktage. (EUR 16.37)
Algorithmic Learning in a Random World describes recent theoretical and experimental developments in building computable approximations to Kolmogorov's algorithmic notion of randomness. B… Plus…
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.; PDF; Computing > Computer programming / software development > Algorithms & data structures, Springer US<
No. 9780387250618. Frais d'envoiInstock, Despatched same working day before 3pm, zzgl. Versandkosten., Livraison non-comprise
Algorithmic Learning in a Random World describes recent theoretical and experimental developments in building computable approximations to Kolmogorov's algorithmic notion of randomness. B… Plus…
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., Springer<
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Algorithmic Learning in a Random World: ab 160.49 € eBooks > Sachthemen & Ratgeber > Computer & Internet Springer-Verlag GmbH eBook als pdf, Springer-Verlag GmbH
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Informations détaillées sur le livre - Algorithmic Learning in a Random World
EAN (ISBN-13): 9780387250618 ISBN (ISBN-10): 0387250611 Date de parution: 2005 Editeur: Springer-Verlag 324 Pages Langue: eng/Englisch
Livre dans la base de données depuis 2008-02-14T13:28:11+01:00 (Paris) Page de détail modifiée en dernier sur 2023-11-06T15:10:32+01:00 (Paris) ISBN/EAN: 0387250611
ISBN - Autres types d'écriture: 0-387-25061-1, 978-0-387-25061-8 Autres types d'écriture et termes associés: Auteur du livre: erl vladimir, venn alexander Titre du livre: learning, world cup, one world
Données de l'éditeur
Auteur: Vladimir Vovk Titre: Algorithmic Learning in a Random World Editeur: Springer; Springer US 324 Pages Date de parution: 2005-12-05 New York; NY; US Imprimé / Fabriqué en Langue: Anglais 176,00 € (DE)
EA; E107; eBook; Nonbooks, PBS / Informatik, EDV/Informatik; Künstliche Intelligenz; Verstehen; Approximation; Conformal prediction; Randomness; Regression; algorithms; classification; learning; machine learning; modeling; B; Artificial Intelligence; Statistics and Computing/Statistics Programs; Data Structures and Information Theory; Artificial Intelligence; Statistics and Computing; Data Structures and Information Theory; Computer Science; Wahrscheinlichkeitsrechnung und Statistik; Mathematische und statistische Software; Algorithmen und Datenstrukturen; Informationstheorie; BB
Conformal prediction is a valuable new method of machine learning. Conformal predictors are among the most accurate methods of machine learning, and unlike other state-of-the-art methods, they provide information about their own accuracy and reliability. This new monograph integrates mathematical theory and revealing experimental work. It demonstrates mathematically the validity of the reliability claimed by conformal predictors when they are applied to independent and identically distributed data, and it confirms experimentally that the accuracy is sufficient for many practical problems. Later chapters generalize these results to models called repetitive structures, which originate in the algorithmic theory of randomness and statistical physics. The approach is flexible enough to incorporate most existing methods of machine learning, including newer methods such as boosting and support vector machines and older methods such as nearest neighbors and the bootstrap. Topics and Features: * Describes how conformal predictors yield accurate and reliable predictions, complemented with quantitative measures of their accuracy and reliability * Handles both classification and regression problems * Explains how to apply the new algorithms to real-world data sets * Demonstrates the infeasibility of some standard prediction tasks * Explains connections with Kolmogorov’s algorithmic randomness, recent work in machine learning, and older work in statistics * Develops new methods of probability forecasting and shows how to use them for prediction in causal networks Researchers in computer science, statistics, and artificial intelligence will find the book an authoritative and rigorous treatment of some of the most promising new developments in machine learning. Practitioners and students in all areas of research that use quantitative prediction or machine learning will learn about important new methods.
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