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Algorithmic Learning in a Random World
Vovk, Vladimir; Gammerman, Alex; Shafer, Glenn
€ 233.81
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Description for Algorithmic Learning in a Random World
Hardback. A scientific monograph that develops algorithmic foundations in machine learning theory. It is intended for researchers and postgraduates in CS, statistics, and AI. Num Pages: 340 pages, 62 black & white illustrations, biography. BIC Classification: UMB; UYQM. Category: (P) Professional & Vocational; (UP) Postgraduate, Research & Scholarly; (UU) Undergraduate. Dimension: 234 x 156 x 20. Weight in Grams: 1450.
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. ... Read more
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. ... Read more
Product Details
Format
Hardback
Publication date
2005
Publisher
Springer-Verlag New York Inc. United States
Number of pages
340
Condition
New
Number of Pages
324
Place of Publication
New York, NY, United States
ISBN
9780387001524
SKU
V9780387001524
Shipping Time
Usually ships in 15 to 20 working days
Ref
99-15
Reviews for Algorithmic Learning in a Random World
From the reviews: "Algorithmic Learning in a Random World has ten chapters, three appendices, and extensive references. Each chapter ends with a section containing comments, historical discussion, and bibliographical remarks. … The material is developed well and reasonably easy to follow … . the text is very readable. … is doubtless an important reference summarizing ... Read more