Overview andGoals Pattern recognition has evolved as a mature field of
data analysis and its practice involves decision making using a wide
variety of machine learning tools. Over the last three decades,
substantial advances have been made in the areas of classification,
prediction, optimisation and planning algorithms. Inparticular, the
advances made in the areas of non-linear classification, statistical
pattern recognition, multi-objective optimisation, string matching and
uncertainty management are notable. These advances have been triggered
by the availability of cheap computing power which allows large
quantities of data to be processed in a very short period of time, and
therefore developed algorithms can be tested easily on real problems.
The current focus of pattern recognition research and development is to
take laboratory solutions to commercial applications. The main goal of
this book is to provide researchers with some of the latest novel
techniques in the area of pattern recognition, and to show the potential
of such techniques on real problems. The book will provide an excellent
background to pattern recognition students and researchers into latest
algorithms for pattern matching, and classification and their practical
applications for imaging and non-imaging applications. Organization and
Features The book is organised in two parts. The first nine chapters of
the book describe novel advances in the areas of graph matching,
information fusion, data clustering and classification, feature
extraction and decision making under uncertainty.
