
Sách keo gáy, Bìa mềm
Thể loại:Computers - Computer Science
Năm:2014
In lần thứ:1
Ngôn ngữ:english
Trang:705 / 704
Comprehensive Coverage of the Entire Area of Classification
Research
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.