mostraligabue
» » Computational Learning and Probabilistic Reasoning

ePub Computational Learning and Probabilistic Reasoning download

by A. Gammerman

ePub Computational Learning and Probabilistic Reasoning download
Author:
A. Gammerman
ISBN13:
978-0471962793
ISBN:
0471962791
Language:
Publisher:
Wiley; 1 edition (July 1996)
Category:
Subcategory:
Computer Science
ePub file:
1483 kb
Fb2 file:
1488 kb
Other formats:
doc rtf mobi docx
Rating:
4.1
Votes:
107

namely probabilistic reasoning and computational learning. Other readers will always be interested in your opinion of the books you've read.

Other readers will always be interested in your opinion of the books you've read. Whether you've loved the book or not, if you give your honest and detailed thoughts then people will find new books that are right for them.

namely probabilistic reasoning and computational learning. Part I covers Generalisation Principles and Learning and describes several new inductive principles and techniques used in computational learning.

Part I covers Generalisation Principles and Learning and describes several new inductive principles and techniques used in computational learning.

Start by marking Computational Learning and Probabilistic Reasoning as Want to Read . This book is devoted to two interrelated techniques in solving some important problems in machine intelligence and pattern recognition, namely probabilistic reasoning and computational learning

Start by marking Computational Learning and Probabilistic Reasoning as Want to Read: Want to Read savin. ant to Read. This book is devoted to two interrelated techniques in solving some important problems in machine intelligence and pattern recognition, namely probabilistic reasoning and computational learning. It is divided into four parts, the first of which describes several new inductive principles and techniques used in computational learning.

First Online: 18 December 1997. Authors and Affiliations. Received 01 January 1997. Accepted 01 January 1997.

Gammerman . Royal Holloway, University of London, Egham, Surrey TW20 OEX U. Royal Holloway, University of London, Egham, Surrey TW20 OEX UK. Gordon . Derbyshire Constabulary Headquarters, Butterley Hall, Ripley, Derbyshire DE5 3RS UK. Grana . University of the Basque Country, Dept. of Computer Science and AI, PO Box 649 E-20080 San Sebastian, Spain. Hoist . Royal Institute of Technology, Studies ofArtifical Neural Systems, Department of Numerical Analysis and Computing Science, S-100 44 Stockholm, Sweden. Kovalenko . Russian Academy of Sciences, Central Economics & Mathematics Institute, ul. Krasikova 32, Moscow 117418 Russia.

Probabilistic Graphical Models: Principles and Techniques (Adaptive Computation and Machine Learning series)

Probabilistic Graphical Models: Principles and Techniques (Adaptive Computation and Machine Learning series). An astonishing machine learning book: intuitive, full of examples, fun to read but still comprehensive, strong and deep! A great starting point for any university student - and a must have for anybody in the field. Jan Peters, Darmstadt University of Technology; Max-Planck Institute for Intelligent Systems). Kevin Murphy excels at unraveling the complexities of machine learning methods while motivating the reader with a stream of illustrated examples and real world case studies.

A. Gammerman, (e. Computational Learning and Probabilistic Reasoning. John Wiley & Sons, Chichester, 1996. 23. Ilia Nouretdinov, Tony Bellotti and Alexander Gammerman.

Finding books BookSee BookSee - Download books for free. Category: engeneering technology.

Other data about this publication. Книги сотрудников ЦЭМИ. Электронные коллекции ресурсов по общественным наукам.

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.