Machine Learning and Data Mining: Methods and Applications - Hardcover

 
9780471971993: Machine Learning and Data Mining: Methods and Applications

Synopsis

Master the new computational tools to get the most out of your information system.

This practical guide, the first to clearly outline the situation for the benefit of engineers and scientists, provides a straightforward introduction to basic machine learning and data mining methods, covering the analysis of numerical, text, and sound data.

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About the Author

Ryszard S. Michalski was a Polish-American computer scientist. Michalski was Professor at George Mason University and a pioneer in the field of machine learning. Ivan Bratko is the editor of Machine Learning and Data Mining: Methods and Applications, published by Wiley.

From the Back Cover

Being able to extract meaningful patterns and strategic knowledge from large stores of information held electronically is increasingly a challenge facing the business and science worlds. In response, methods are being developed for discovering patterns and general rules from the information in databases, data warehouses and document information systems. This book is the first major text dedicated to issues at the intersection of machine learning and data mining - two interrelated fields that provide the foundations for these methods. Written by a team of international experts Machine Learning and Data Mining presents an exciting contribution addressing the new challenge. It provides an introduction to basic methods and its coverage spans the analysis of numerical data, text, images and sound. Most impressively, the book describes applications across a wide spectrum of real world problems, in domains such as engineering, control, biology, medicine and music

From the Inside Flap

Being able to extract meaningful patterns and strategic knowledge from large stores of information held electronically is increasingly a challenge facing the business and science worlds. In response, methods are being developed for discovering patterns and general rules from the information in databases, data warehouses and document information systems. This book is the first major text dedicated to issues at the intersection of machine learning and data mining - two interrelated fields that provide the foundations for these methods. Written by a team of international experts Machine Learning and Data Mining presents an exciting contribution addressing the new challenge. It provides an introduction to basic methods and its coverage spans the analysis of numerical data, text, images and sound. Most impressively, the book describes applications across a wide spectrum of real world problems, in domains such as engineering, control, biology, medicine and music

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