computational learning probabilistic reasoning (20 résultats)

Etat : Poor. This is an ex-library book and may have the usual library/used-book markings inside.This book has hardback covers. Book contains highlighter markings. In poor condition, suitable as a reading copy. Please note the Image in this listing is a stock photo and may not match the covers of the actual item,900grams, ISBN:9780471962793.

Hardcover. Etat : Very Good. 1. It's a well-cared-for item that has seen limited use. The item may show minor signs of wear. All the text is legible, with all pages included. It may have slight markings and/or highlighting.

Etat : Very Good. Text block firm and clean, binding unblemished, boards straight, without highlights or underlining. Very clean, nearly like new. Without any discs, access codes or extra items. Previous library copy with usual markings/stickers or stamps. Supporting Bay Area Friends of the Library since 2010. Well packaged and promptly shipped.

Etat : new.

HRD. Etat : New. New Book. Shipped from UK. Established seller since 2000.

Etat : New.

Etat : New.

Etat : New. In.

Etat : As New. Unread book in perfect condition.

Etat : As New. Unread book in perfect condition.

Brossura. Etat : fine. West Sussex, 1996; pp. 312. Libro.

Etat : New. pp. 338.

Etat : New. This text is devoted to two interrelated techniques used in solving some important problems in machine intelligence and pattern recognition. Editor(s): Gammerman, A. Num Pages: 338 pages, Illustrations. BIC Classification: PBT; UYA; UYQM. Category: (P) Professional & Vocational; (UP) Postgraduate, Research & Scholarly; (UU) Undergraduate. Dimension: 257 x 183 x 25. Weight in Grams: 776. . 1996. 1st Edition. Hardcover. . . . .

Etat : New. pp. 338.

Hardcover. Etat : Brand New. 1st edition. 338 pages. 10.00x7.00x1.00 inches. In Stock.

Buch. Etat : Neu. Neuware - Providing a unified coverage of the latest research and applications methods and techniques, this book is devoted to two interrelated techniques for solving some important problems in machine intelligence and pattern recognition, namely probabilistic reasoning and computational learning. The contributions in this volume describe and explore the current developments in computer science and theoretical statistics which provide computational probabilistic models for manipulating knowledge found in industrial and business data. These methods are very efficient for handling complex problems in medicine, commerce and finance. Part I covers Generalisation Principles and Learning and describes several new inductive principles and techniques used in computational learning. Part II describes Causation and Model Selection including the graphical probabilistic models that exploit the independence relationships presented in the graphs, and applications of Bayesian networks to multivariate statistical analysis. Part III includes case studies and descriptions of Bayesian Belief Networks and Hybrid Systems. Finally, Part IV on Decision-Making, Optimization and Classification describes some related theoretical work in the field of probabilistic reasoning. Statisticians, IT strategy planners, professionals and researchers with interests in learning, intelligent databases and pattern recognition and data processing for expert systems will find this book to be an invaluable resource. Real-life problems are used to demonstrate the practical and effective implementation of the relevant algorithms and techniques.

Etat : New. This text is devoted to two interrelated techniques used in solving some important problems in machine intelligence and pattern recognition. Editor(s): Gammerman, A. Num Pages: 338 pages, Illustrations. BIC Classification: PBT; UYA; UYQM. Category: (P) Professional & Vocational; (UP) Postgraduate, Research & Scholarly; (UU) Undergraduate. Dimension: 257 x 183 x 25. Weight in Grams: 776. . 1996. 1st Edition. Hardcover. . . . . Books ship from the US and Ireland.

Hardcover. Etat : new. Hardcover. Providing a unified coverage of the latest research and applications methods and techniques, this book is devoted to two interrelated techniques for solving some important problems in machine intelligence and pattern recognition, namely probabilistic reasoning and computational learning. The contributions in this volume describe and explore the current developments in computer science and theoretical statistics which provide computational probabilistic models for manipulating knowledge found in industrial and business data. These methods are very efficient for handling complex problems in medicine, commerce and finance. Part I covers Generalisation Principles and Learning and describes several new inductive principles and techniques used in computational learning. Part II describes Causation and Model Selection including the graphical probabilistic models that exploit the independence relationships presented in the graphs, and applications of Bayesian networks to multivariate statistical analysis. Part III includes case studies and descriptions of Bayesian Belief Networks and Hybrid Systems. Finally, Part IV on Decision-Making, Optimization and Classification describes some related theoretical work in the field of probabilistic reasoning. Statisticians, IT strategy planners, professionals and researchers with interests in learning, intelligent databases and pattern recognition and data processing for expert systems will find this book to be an invaluable resource. Real-life problems are used to demonstrate the practical and effective implementation of the relevant algorithms and techniques. This text is devoted to two interrelated techniques used in solving some important problems in machine intelligence and pattern recognition. This item is printed on demand. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.

Hardcover. Etat : new. Hardcover. Providing a unified coverage of the latest research and applications methods and techniques, this book is devoted to two interrelated techniques for solving some important problems in machine intelligence and pattern recognition, namely probabilistic reasoning and computational learning. The contributions in this volume describe and explore the current developments in computer science and theoretical statistics which provide computational probabilistic models for manipulating knowledge found in industrial and business data. These methods are very efficient for handling complex problems in medicine, commerce and finance. Part I covers Generalisation Principles and Learning and describes several new inductive principles and techniques used in computational learning. Part II describes Causation and Model Selection including the graphical probabilistic models that exploit the independence relationships presented in the graphs, and applications of Bayesian networks to multivariate statistical analysis. Part III includes case studies and descriptions of Bayesian Belief Networks and Hybrid Systems. Finally, Part IV on Decision-Making, Optimization and Classification describes some related theoretical work in the field of probabilistic reasoning. Statisticians, IT strategy planners, professionals and researchers with interests in learning, intelligent databases and pattern recognition and data processing for expert systems will find this book to be an invaluable resource. Real-life problems are used to demonstrate the practical and effective implementation of the relevant algorithms and techniques. This text is devoted to two interrelated techniques used in solving some important problems in machine intelligence and pattern recognition. This item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.

Hardcover. Etat : Brand New. 1st edition. 338 pages. 10.00x7.00x1.00 inches. In Stock. This item is printed on demand.