- Bayesian Network
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- Editor
Ahmed Rebai
(ISBN) 978-953-307-124-4
432 pages, August, 2010
Downloads: 4498
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- Bayesian networks are a very general and powerful tool that can be used for a large number of problems involving uncertainty: reasoning, learning, planning and perception. They provide a language that supports efficient algorithms for the automatic c ...
- Kalman Filter
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- Editor
Vedran Kordic
(ISBN) 978-953-307-094-0
390 pages, May, 2010
Downloads: 5690
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- The Kalman filter has been successfully employed in diverse areas of study over the
last 50 years and the chapters in this book review its recent applications. The editors hope the
selected works will be useful to readers, contributing to future d ...
- New Advances in Machine Learning
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- Editor
Yagang Zhang
(ISBN) 978-953-307-034-6
366 pages, February, 2010
Downloads: 4471
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- The purpose of this book is to provide an up-to-date and systematical introduction to the principles and algorithms of machine learning. The definition of learning is broad enough to include most tasks that we commonly call “learning” tasks, as ...
- Pattern Recognition Recent Advances
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- Editor
Adam Herout
(ISBN) 978-953-7619-90-9
524 pages, February, 2010
Downloads: 2510
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- Nos aute magna at aute doloreetum erostrud eugiam zzriuscipsum dolorper iliquate velit ad magna feugiamet, quat lore dolore modolor ipsum vullutat lorper sim inci blan vent utet, vero er sequatum delit lortion sequip eliquatet ilit aliquip eui blam, ...
- Robot Vision
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- Editor
Ales Ude
(ISBN) 978-953-307-077-3
614 pages, March, 2010
Downloads: 4734
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- The purpose of robot vision is to enable robots to perceive the external world in order
to perform a large range of tasks such as navigation, visual servoing for object
tracking and manipulation, object recognition and categorization, surveillan ...
- Self-Organizing Maps
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- Editor
George K Matsopoulos
(ISBN) 978-953-307-074-2
430 pages, April, 2010
Downloads: 3961
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- The Self-Organizing Map (SOM) is a neural network algorithm, which uses a competitive learning technique to train itself in an unsupervised manner. SOMs are different from other artificial neural networks in the sense that they use a neighborhood fun ...
