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Combining Pattern Classifiers: Methods and Algorithms, by Ludmila I. Kuncheva
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Covering pattern classification methods, Combining Classifiers: Ideas and Methods focuses on the important and widely studied issue of how to combine several classifiers together in order to achieve improved recognition performance. It is one of the first books to provide unified, coherent, and expansive coverage of the topic and as such will be welcomed by those involved in the area. With case studies that bring the text alive and demonstrate 'real-world' applications it is destined to become essential reading.
- Sales Rank: #3524848 in Books
- Published on: 2004-07-01
- Original language: English
- Number of items: 1
- Dimensions: 9.47" h x 1.09" w x 6.48" l, 1.45 pounds
- Binding: Hardcover
- 376 pages
Review
The well written 'Combining Pattern Classifiers'…is all about how patterns are to be recognized and interpreted." (Journal of Statistical Computation and Simulation, March 2006)
"In a clear and straightforward manner, the author provides a much-needed road map through a multifaceted and often controversial subject…" (International Journal of General Systems, June 2005)
"The book is very interesting and is written on a high scientific level…" (Mathematical Reviews, 2006c)
"...a unique and perfect book for researchers and practitioners who want to get a grasp of this new exciting area." (Journal of Intelligent & Fuzzy Systems, Vol. 16, No. 2, 2005)
"...a well-written and timely book, the first of its kind...a welcome contribution to pattern recognition...I would keep this book in my library." (Technometrics, November 2005)
"…destined to become essential reading…" (Zentralblatt MATH, Vol. 1066 (17), 2005)
From the Back Cover
A unified, coherent, and expansive treatment of current classifier ensemble methods
Mail sorting, medical test reading, military target recognition, signature verification, meteorological forecast, DNA matching, fingerprint recognition. These are just a few of the areas requiring reliable, precise pattern recognition.
Although in the past, pattern recognition has focused on designing single classifiers, recently the focus has been on combining several classifiers and getting a consensus of results for greater accuracy. This interest in combining classifiers has grown astronomically in recent years, evolving into a rich and dynamic, if loosely structured, discipline. Combining Pattern Classifiers: Methods and Algorithms represents the first attempt to provide a comprehensive survey of this fast-growing field. In a clear and straightforward manner, the author provides a much-needed road map through a multifaceted and often controversial subject while effectively organizing and systematizing the current state of the art.
Covering a broad range of methodologies, algorithms, and theories, the text addresses such questions as:
- Why should we combine classifiers?
- What are the current approaches for building classifier ensembles?
- What fusion methods can we use?
- How do we measure diversity in a classifier ensemble and is diversity really a key factor to its success?
Replete with case studies and real-world applications, this groundbreaking text will be of interest to academics and researchers in the field seeking both new classification tools and new uses for the old ones.
About the Author
LUDMILA I. KUNCHEVA is a Senior Lecturer in the School of Informatics at the University of Wales, Bangor, UK.
Most helpful customer reviews
3 of 6 people found the following review helpful.
Comprehensive and Current Coverage of Model Combination
By Amazon Customer
This title covers a variety of predictive model combination methods, for both categorical and numeric target variables (bagging, boosting, etc.). It uses specific cases to illustrate particular points and makes reference to current literature (many references are from the early 2000s). Some MATLAB source code is supplied, but not on a machine readable medium.
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