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Data Mining: Practical Machine Learning Tools And Techniques: Practical Machine Learning Tools and Techniques, Second Edition - Brossura

 
9780120884070: Data Mining: Practical Machine Learning Tools And Techniques: Practical Machine Learning Tools and Techniques, Second Edition

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As with any burgeoning technology that enjoys commercial attention, the use of data mining is surrounded by a great deal of hype. Exaggerated reports tell of secrets that can be uncovered by setting algorithms loose on oceans of data. But there is no magic in machine learning, no hidden power, no alchemy. Instead there is an identifiable body of practical techniques that can extract useful information from raw data. This book describes these techniques and shows how they work.

The book is a major revision of the first edition that appeared in 1999. While the basic core remains the same, it has been updated to reflect the changes that have taken place over five years, and now has nearly double the references. The highlights for the new edition include thirty new technique sections; an enhanced Weka machine learning workbench, which now features an interactive interface; comprehensive information on neural networks; a new section on Bayesian networks; plus much more.

* Algorithmic methods at the heart of successful data mining-including tried and true techniques as well as leading edge methods
* Performance improvement techniques that work by transforming the input or output
* Downloadable Weka, a collection of machine learning algorithms for data mining tasks, including tools for data pre-processing, classification, regression, clustering, association rules, and visualization-in a new, interactive interface

Le informazioni nella sezione "Riassunto" possono far riferimento a edizioni diverse di questo titolo.

Informazioni sugli autori

Ian H. Witten is a professor of computer science at the University of Waikato in New Zealand. He directs the New Zealand Digital Library research project. His research interests include information retrieval, machine learning, text compression, and programming by demonstration. He received an MA in Mathematics from Cambridge University, England; an MSc in Computer Science from the University of Calgary, Canada; and a PhD in Electrical Engineering from Essex University, England. He is a fellow of the ACM and of the Royal Society of New Zealand. He has published widely on digital libraries, machine learning, text compression, hypertext, speech synthesis and signal processing, and computer typography.

Eibe Frank lives in New Zealand with his Samoan spouse and two lovely boys, but originally hails from Germany, where he received his first degree in computer science from the University of Karlsruhe. He moved to New Zealand to pursue his Ph.D. in machine learning under the supervision of Ian H. Witten and joined the Department of Computer Science at the University of Waikato as a lecturer on completion of his studies. He is now a professor at the same institution. As an early adopter of the Java programming language, he laid the groundwork for the Weka software described in this book. He has contributed a number of publications on machine learning and data mining to the literature and has refereed for many conferences and journals in these areas.

Dalla quarta di copertina

This book presents this new discipline in a very accessible form: both as a text to train the next generation of practitioners and researchers, and to inform lifelong learners like myself. Witten and Frank have a passion for simple and elegant solutions. They approach each topic with this mindset, grounding all concepts in concrete examples, and urging the reader to consider the simple techniques first, and then progress to the more sophisticated ones if the simple ones prove inadequate.

If you have data that you want to analyze and understand, this book and the associated Weka toolkit are an excellent way to start.

--From the foreword by Jim Gray, Microsoft Research

As with any burgeoning technology that enjoys commercial attention, the use of data mining is surrounded by a great deal of hype. Exaggerated reports tell of secrets that can be uncovered by setting algorithms loose on oceans of data. But there is no magic in machine learning, no hidden power, no alchemy. Instead there is an identifiable body of practical techniques that can extract useful information from raw data. This book describes these techniques and shows how they work.

The book is a major revision of the first edition that appeared in 1999. While the basic core remains the same, it has been updated to reflect the changes that have taken place over five years, and now has nearly double the references. The highlights for the new edition include thirty new technique sections; an enhanced Weka machine learning workbench, which now features an interactive interface; comprehensive information on neural networks; a new section on Bayesian networks; plus much more.

Offering a thorough grounding in machine learning concepts as well as practical advice on applying the tools and techniques, inside you ll find:

+ Algorithmic methods at the heart of successful data mining including tried and true techniques as well as leading edge methods;
+ Performance improvement techniques that work by transforming the input or output;
+ Downloadable Weka, a collection of machine learning algorithms for data mining tasks, including tools for data pre-processing, classification, regression, clustering, association rules, and visualization in a new, interactive interface.|This book presents this new discipline in a very accessible form: both as a text to train the next generation of practitioners and researchers, and to inform lifelong learners like myself. Witten and Frank have a passion for simple and elegant solutions. They approach each topic with this mindset, grounding all concepts in concrete examples, and urging the reader to consider the simple techniques first, and then progress to the more sophisticated ones if the simple ones prove inadequate.

If you have data that you want to analyze and understand, this book and the associated Weka toolkit are an excellent way to start.

--From the foreword by Jim Gray, Microsoft Research

As with any burgeoning technology that enjoys commercial attention, the use of data mining is surrounded by a great deal of hype. Exaggerated reports tell of secrets that can be uncovered by setting algorithms loose on oceans of data. But there is no magic in machine learning, no hidden power, no alchemy. Instead there is an identifiable body of practical techniques that can extract useful information from raw data. This book describes these techniques and shows how they work.

The book is a major revision of the first edition that appeared in 1999. While the basic core remains the same, it has been updated to reflect the changes that have taken place over five years, and now has nearly double the references. The highlights for the new edition include thirty new technique sections; an enhanced Weka machine learning workbench, which now features an interactive interface; comprehensive information on neural networks; a new section on Bayesian networks; plus much more.

Offering a thorough grounding in machine learning concepts as well as practical advice on applying the tools and techniques, inside you ll find:

+ Algorithmic methods at the heart of successful data mining including tried and true techniques as well as leading edge methods;
+ Performance improvement techniques that work by transforming the input or output;
+ Downloadable Weka, a collection of machine learning algorithms for data mining tasks, including tools for data pre-processing, classification, regression, clustering, association rules, and visualization in a new, interactive interface.

Le informazioni nella sezione "Su questo libro" possono far riferimento a edizioni diverse di questo titolo.

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9783446215337: Data Mining: Praktische Werkzeuge und Techniken für das maschinelle Lernen

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ISBN 10:  3446215336 ISBN 13:  9783446215337
Casa editrice: Carl Hanser Verlag GmbH & Co..., 2001
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Condizione: Good. This is an ex-library book and may have the usual library/used-book markings inside.This book has soft covers. Clean from markings. In good all round condition. Please note the Image in this listing is a stock photo and may not match the covers of the actual item,1250grams, ISBN:9780120884070. Codice articolo 9830346

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Condizione: Fair. This is an ex-library book and may have the usual library/used-book markings inside.This book has soft covers. In fair condition, suitable as a study copy. Please note the Image in this listing is a stock photo and may not match the covers of the actual item,1300grams, ISBN:9780120884070. Codice articolo 8722650

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Paperback. Condizione: Very Good. Data Mining, Second Edition, describes data mining techniques and shows how they work. The book is a major revision of the first edition that appeared in 1999. While the basic core remains the same, it has been updated to reflect the changes that have taken place over five years, and now has nearly double the references. The highlights of this new edition include thirty new technique sections; an enhanced Weka machine learning workbench, which now features an interactive interface; comprehensive information on neural networks; a new section on Bayesian networks; and much more. This text is designed for information systems practitioners, programmers, consultants, developers, information technology managers, specification writers as well as professors and students of graduate-level data mining and machine learning courses. The book has been read, but is in excellent condition. Pages are intact and not marred by notes or highlighting. The spine remains undamaged. Codice articolo GOR005523038

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