Machine Learning: Theory and Applications
：Machine Learning: Theory and Applications
( Volume 31 )
Publication subTitle ：Machine Learning: Theory and Applications
Publication series ：Volume 31
Author： Rao C. R.;Govindaraju Venu
Publisher： Elsevier Science
Publication year： 2013
E-ISBN: 9780444538666
P-ISBN(Paperback): 9780444538598
P-ISBN(Hardback): 9780444538598
Subject： F224-39 computer applications;O211 probability (probability theory, probability theory);TP Automation Technology , Computer Technology;TP3 Computers
Language： ENG
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Description
Statistical learning and analysis techniques have become extremely important today, given the tremendous growth in the size of heterogeneous data collections and the ability to process it even from physically distant locations. Recent advances made in the field of machine learning provide a strong framework for robust learning from the diverse corpora and continue to impact a variety of research problems across multiple scientific disciplines. The aim of this handbook is to familiarize beginners as well as experts with some of the recent techniques in this field.
The Handbook is divided in two sections: Theory and Applications, covering machine learning, data analytics, biometrics, document recognition and security.
- very relevant to current research challenges faced in various fields
- self-contained reference to machine learning
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emphasis on applications-oriented techniques