Machine Learning and Statistics: The Interface - Rilegato

European Conference On Machine Learning (1994 Catania, Italy)

 
9780471148906: Machine Learning and Statistics: The Interface
Rilegato
ISBN 10: 0471148903 ISBN 13: 9780471148906
Casa editrice: Wiley-Interscience, 1996

Sinossi

Demonstrates that machine learning and statistics share several areas of common research, including classification, prediction, and control, and that statistical and probability methodologies can be used to develop different machine learning algorithms, which can in turn be used by statisticians to perform classification and forecasting tasks. Most of the 14 papers have been revised from presentations at a 1994 workshop in Catania, Sicily to make them accessible to novices in the field. Annotation c. by Book News, Inc., Portland, Or.

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Informazioni sull?autore

About the editors

G. Nakhaeizadeh is Senior Scientist at the Daimler-Benz Research Center in Ulm, Germany, and Professor at Karlsruhe University. From 1990 to 1993 he directed the Machine Learning Project StatLog, which was supported by the European Union. His research interests center on symbolic and statistical learning and their industrial and commercial applications.

C. C. Taylor is Senior Lecturer in the Department of Statistics at the University of Leeds, U.K. His particular interests include nonparametric density estimation methods related to classification and statistical methods in image analysis.

Dalla quarta di copertina

The interface between statistics and machine learning (ML) is an increasingly popular research subject, as scientists and statisticians discover useful areas where these disciplines overlap. ML deals primarily with learning rules or structure, and while some books on the subject exist, this volume is the only one to integrate ML with statistics. It explores new areas where theory and methods can be shared and demonstrates the benefits to those working in either discipline. Written by leading experts in both fields, Machine Learning and Statistics is a result of the authors participation in the 1994 European Conference in Machine Learning. This important collection of contributions was adapted from conference workshop material and reworked to address readers of diverse backgrounds and skills. For newcomers to the field, a thorough introduction surveys the various topics and supplies numerous references for further reading. The books main focus is on classification, the most common area of intersection. The classification process uses information about a new example to assign the example to one of a known number of classes. Such methods typically involve a rule learned from an initial set of data, which is where ML comes into play. Other topics covered include prediction, control, and an introduction to methods of knowledge discovery in data-bases a skill that has become especially relevant with the explosion in large-scale databases. Timely, practical, and innovative, this book offers a number of new algorithms and draws on real-world examples including financial and medical applications. It also includes two chapters on loans/credit applications that help identify bad risks and good customersuseful for those working with credit scoring and bad debt analysis. Machine Learning and Statistics is an invaluable resource for researchers involved with artificial intelligence and ML in academia, government, or industry, as well as those working with pattern recognition in statistical departments; for students at the graduate level who seek to expand their horizons; and for anyone who would like to learn more about these cutting-edge methodologies.

Dal risvolto di copertina interno

The interface between statistics and machine learning (ML) is an increasingly popular research subject, as scientists and statisticians discover useful areas where these disciplines overlap. ML deals primarily with learning rules or structure, and while some books on the subject exist, this volume is the only one to integrate ML with statistics. It explores new areas where theory and methods can be shared and demonstrates the benefits to those working in either discipline. Written by leading experts in both fields, Machine Learning and Statistics is a result of the authors participation in the 1994 European Conference in Machine Learning. This important collection of contributions was adapted from conference workshop material and reworked to address readers of diverse backgrounds and skills. For newcomers to the field, a thorough introduction surveys the various topics and supplies numerous references for further reading. The books main focus is on classification, the most common area of intersection. The classification process uses information about a new example to assign the example to one of a known number of classes. Such methods typically involve a rule learned from an initial set of data, which is where ML comes into play. Other topics covered include prediction, control, and an introduction to methods of knowledge discovery in data-bases a skill that has become especially relevant with the explosion in large-scale databases. Timely, practical, and innovative, this book offers a number of new algorithms and draws on real-world examples including financial and medical applications. It also includes two chapters on loans/credit applications that help identify bad risks and good customersuseful for those working with credit scoring and bad debt analysis. Machine Learning and Statistics is an invaluable resource for researchers involved with artificial intelligence and ML in academia, government, or industry, as well as those working with pattern recognition in statistical departments; for students at the graduate level who seek to expand their horizons; and for anyone who would like to learn more about these cutting-edge methodologies.

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Editore
Wiley-Interscience
Data di pubblicazione
1996
Lingua
Inglese
ISBN 10 
0471148903
ISBN 13 
9780471148906
Rilegatura
Copertina rigida
Numero di pagine
343
Redattore
Nakhaeizadeh G., Taylor C. C.
Contatto del produttore
non disponibile
Persona responsabile
non disponibile