| Algorithmic Learning in a Random World 2005 Edition Contributor(s): Vovk, Vladimir (Author), Gammerman, Alex (Author), Shafer, Glenn (Author) |
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ISBN: 0387001522 ISBN-13: 9780387001524 Publisher: Springer OUR PRICE: $189.99 Product Type: Hardcover - Other Formats Published: March 2005 Annotation: 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. |
| Additional Information |
| BISAC Categories: - Computers | Intelligence (ai) & Semantics - Computers | Computer Science - Mathematics | Probability & Statistics - General |
| Dewey: 519.287 |
| LCCN: 2005042556 |
| Physical Information: 0.95" H x 6.4" W x 9.56" (1.53 lbs) 324 pages |
| Descriptions, Reviews, Etc. |
| Publisher 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. |