Combining Pattern Classifiers: Methods and Algorithms (2)
By:
Sign Up Now!
Already a Member? Log In
You must be logged into UK education collection to access this title.
Learn about membership options,
or view our freely available titles.
- Synopsis
- A unified, coherent treatment of current classifier ensemble methods, from fundamentals of pattern recognition to ensemble feature selection, now in its second edition The art and science of combining pattern classifiers has flourished into a prolific discipline since the first edition of Combining Pattern Classifiers was published in 2004. Dr. Kuncheva has plucked from the rich landscape of recent classifier ensemble literature the topics, methods, and algorithms that will guide the reader toward a deeper understanding of the fundamentals, design, and applications of classifier ensemble methods. Thoroughly updated, with MATLABĀ® code and practice data sets throughout, Combining Pattern Classifiers includes: Coverage of Bayes decision theory and experimental comparison of classifiers Essential ensemble methods such as Bagging, Random forest, AdaBoost, Random subspace, Rotation forest, Random oracle, and Error Correcting Output Code, among others Chapters on classifier selection, diversity, and ensemble feature selection With firm grounding in the fundamentals of pattern recognition, and featuring more than 140 illustrations, Combining Pattern Classifiers, Second Edition is a valuable reference for postgraduate students, researchers, and practitioners in computing and engineering.
- Copyright:
- 2014
Book Details
- Book Quality:
- Publisher Quality
- ISBN-13:
- 9781118914540
- Related ISBNs:
- 9781118914564, 9781118315231
- Publisher:
- Wiley
- Date of Addition:
- 07/29/19
- Copyrighted By:
- Wiley
- Adult content:
- No
- Language:
- English
- Has Image Descriptions:
- No
- Categories:
- Nonfiction, Computers and Internet, Technology
- Submitted By:
- Bookshare Staff
- Usage Restrictions:
- This is a copyrighted book.