Comprehensive Coverage of the Entire Area of ClassificationResearch
on the problem of classification tends to be fragmented across such
areas as pattern recognition, database, data mining, and machine
learning. Addressing the work of these different communities in a
unified way, Data Classification: Algorithms and Applications
explores the underlying algorithms of classification as well as
applications of classification in a variety of problem domains,
including text, multimedia, social network, and biological data.
This comprehensive book focuses on three primary aspects of data
classification: Methods: The book first describes common techniques
used for classification, including probabilistic methods, decision
trees, rule-based methods, instance-based methods, support vector
machine methods, and neural networks. Domains: The book then
examines specific methods used for data domains such as multimedia,
text, time-series, network, discrete sequence, and uncertain data.
It also covers large data sets and data streams due to the recent
importance of the big data paradigm. Variations: The book concludes
with insight on variations of the classification process. It
discusses ensembles, rare-class learning, distance function
learning, active learning, visual learning, transfer learning, and
semi-supervised learning as well as evaluation aspects of
classifiers.
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