Computational Learning and Probabilistic Reasoning (Hardcover)

Product Description
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.

From the Back Cover
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.

First Sentence
The structure of a learning theory as well as the main concepts of the theory are described. Read the first page Browse Sample Pages
Front Cover | Copyright | Table of Contents | Excerpt | Index | Back Cover

Search inside this book:

There are no customer reviews yet.   Create your own review
Video reviews New feature! Amazon now allows customers to upload product video reviews. Use a webcam or video camera to record and upload reviews to Amazon.	Ad feedback

• Books > Computing & Internet > Computer Science > Artificial Intelligence > Machine Learning
• Books > Computing & Internet > Computer Science > Information Systems
• Books > Computing & Internet > Programming > Algorithms > Artificial Intelligence
• Books > Computing & Internet > Programming > Algorithms > Machine Learning
• Books > Science & Nature > Mathematics > Education > Higher Education
• Books > Science & Nature > Mathematics > Probability & Statistics
• Books > Science & Nature > Popular Science > Maths
• Books > Scientific, Technical & Medical > Mathematics > Applied Mathematics > Statistics & Probability
• Books > Study Books > FE & College > Vocational > Sport & Recreation

• Would you like to update product info or give feedback on images?
• I am the Author, and I want to comment on my book.
• I am the Publisher, and I want to comment on this book.