Imbalanced learning foundations, algorithms, and applications

Solving imbalanced learning problems is critical in numerous data-intensive networked systems, including surveillance, security, Internet, finance, biomedical, and defense, to name a few. The first comprehensive look at this new branch of machine learning, this volume offers a critical review of the...

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Bibliographic Details
Other Authors: He, Haibo, 1976- (-), Ma, Yunqian
Format: eBook
Language:Inglés
Published: Hoboken, NJ : Wiley c2013.
Subjects:
See on Biblioteca Universitat Ramon Llull:https://discovery.url.edu/permalink/34CSUC_URL/1im36ta/alma991009665113706719
Description
Summary:Solving imbalanced learning problems is critical in numerous data-intensive networked systems, including surveillance, security, Internet, finance, biomedical, and defense, to name a few. The first comprehensive look at this new branch of machine learning, this volume offers a critical review of the problem of imbalanced learning, covering the state-of-the-art in techniques, principles, and real-world applications. Scientists and engineers will learn how to tackle the problem of learning from imbalanced datasets, and gain insight into current developments in the field as well as future research.--[Source inconnue]
Item Description:Description based upon print version of record.
Physical Description:1 online resource (224 p.)
Also available in print
Bibliography:Includes bibliographical references and index.
ISBN:9781118646335
9781118646205