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Modern Issues and Methods in BiostatisticsData Mining and Signal Detection

Modern Issues and Methods in Biostatistics: Data Mining and Signal Detection [Data mining, a the confluence of multiple intertwined disciplines such as statistics, machine learning, pattern recognition, database systems, information retrieval, the World Wide Web, visualization, and many application domains, has made great progress in the past decade. Statistics plays a vital role in successful machine learning or data mining in which we are constantly dealing with streamed data, which are usually of vast volume, changing dynamically, possibly infinite, and containing multidimensional features. The contents and methods to be discussed in this chapter often appear under bioinformatics, data mining, signal detection, or pharmacovigilance.] http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png

Modern Issues and Methods in BiostatisticsData Mining and Signal Detection

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Publisher
Springer New York
Copyright
© Springer Science+Business Media, LLC 2011
ISBN
978-1-4419-9841-5
Pages
205 –231
DOI
10.1007/978-1-4419-9842-2_8
Publisher site
See Chapter on Publisher Site

Abstract

[Data mining, a the confluence of multiple intertwined disciplines such as statistics, machine learning, pattern recognition, database systems, information retrieval, the World Wide Web, visualization, and many application domains, has made great progress in the past decade. Statistics plays a vital role in successful machine learning or data mining in which we are constantly dealing with streamed data, which are usually of vast volume, changing dynamically, possibly infinite, and containing multidimensional features. The contents and methods to be discussed in this chapter often appear under bioinformatics, data mining, signal detection, or pharmacovigilance.]

Published: Jun 16, 2011

Keywords: Random Forest; Cellular Automaton; Reinforcement Learning; Adverse Event Reporting System; Proportional Reporting Ratio

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