Imbalanced Learning
Foundations, Algorithms, and Applications
The first book of its kind to review the current status and future direction of the exciting new branch of machine learning/data mining called imbalanced learning Imbalanced learning focuses on how an intelligent system can learn when it is provided with imbalanced data. Solving imbalanced learning problems is critical in numerous data-intensive networked systems, including surveillance, security, Internet, finance, biomedical, defense, and more. Due to the inherent complex characteristics of imbalanced data sets, learning from such data requires new understandings, principles, algorithms, and tools to transform vast amounts of raw data effic…
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Produktdetails
Weitere Autoren: Ma, Yunqian (Hrsg.)
- ISBN: 978-1-118-64633-5
- EAN: 9781118646335
- Produktnummer: 16369058
- Verlag: Wiley
- Sprache: Englisch
- Erscheinungsjahr: 2013
- Seitenangabe: 216 S.
- Plattform: EPUB
- Masse: 5'678 KB
Über den Autor
HAIBO HE, PhD, is an Associate Professor in the Department of Electrical, Computer, and Biomedical Engineering at the University of Rhode Island. He received the National Science Foundation (NSF) CAREER Award and Providence Business News (PBN) Rising Star Innovator Award. YUNQIAN MA PhD, is a senior principal research scientist of Honeywell Labs at Honeywell Inter-national, Inc. He received the International Neural Network Society (INNS) Young Investigator Award.
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