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Foundations of Computational Intelligence Volume 5 - Function Approximation and Classification (Paperback, 2009 ed.)
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Foundations of Computational Intelligence Volume 5 - Function Approximation and Classification (Paperback, 2009 ed.)
Series: Studies in Computational Intelligence, 205
Expected to ship within 10 - 15 working days
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Foundations of Computational Intelligence Volume 5: Function
Approximation and Classification Approximation theory is that area
of analysis which is concerned with the ability to approximate
functions by simpler and more easily calculated functions. It is an
area which, like many other fields of analysis, has its primary
roots in the mat- matics. The need for function approximation and
classification arises in many branches of applied mathematics,
computer science and data mining in particular. This edited volume
comprises of 14 chapters, including several overview Ch- ters,
which provides an up-to-date and state-of-the art research covering
the theory and algorithms of function approximation and
classification. Besides research ar- cles and expository papers on
theory and algorithms of function approximation and classification,
papers on numerical experiments and real world applications were
also encouraged. The Volume is divided into 2 parts: Part-I:
Function Approximation and Classification - Theoretical Foundations
Part-II: Function Approximation and Classification - Success
Stories and Real World Applications Part I on Function
Approximation and Classification - Theoretical Foundations contains
six chapters that describe several approaches Feature Selection,
the use Decomposition of Correlation Integral, Some Issues on
Extensions of Information and Dynamic Information System and a
Probabilistic Approach to the Evaluation and Combination of
Preferences Chapter 1 "Feature Selection for Partial Least Square
Based Dimension Red- tion" by Li and Zeng investigate a systematic
feature reduction framework by combing dimension reduction with
feature selection. To evaluate the proposed framework authors used
four typical data sets.
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