This book considers classical and current theory and practice,
of supervised, unsupervised and semi-supervised pattern
recognition, to build a complete background for professionals and
students of engineering. The authors, leading experts in the field
of pattern recognition, have provided an up-to-date, self-contained
volume encapsulating this wide spectrum of information. The very
latest methods are incorporated in this edition: semi-supervised
learning, combining clustering algorithms, and relevance
feedback.
. Thoroughly developed to include many more worked examples to
give greater understanding of the various methods and
techniques
. Many more diagrams included--now in two color--to provide
greater insight through visual presentation
. Matlab code of the most common methods are given at the end of
each chapter.
. More Matlab code is available, together with an accompanying
manual, via this site
. Latest hot topics included to further the reference value of
the text including non-linear dimensionality reduction techniques,
relevance feedback, semi-supervised learning, spectral clustering,
combining clustering algorithms.
. An accompanying book with Matlab code of the most common
methods and algorithms in the book, together with a descriptive
summary, and solved examples including real-life data sets in
imaging, and audio recognition. The companion book will be
available separately or at a special packaged price (ISBN:
9780123744869).
Thoroughly developed to include many more worked examples to give
greater understanding of the various methods and techniques Many
more diagrams included--now in two color--to provide greater
insight through visual presentation Matlab code of the most common
methods are given at the end of each chapter An accompanying book
with Matlab code of the most common methods and algorithms in the
book, together with a descriptive summary and solved examples, and
including real-life data sets in imaging and audio recognition. The
companion book is available separately or at a special packaged
price (Book ISBN: 9780123744869. Package ISBN: 9780123744913)
Latest hot topics included to further the reference value of the
text including non-linear dimensionality reduction techniques,
relevance feedback, semi-supervised learning, spectral clustering,
combining clustering algorithms Solutions manual, powerpoint
slides, and additional resources are available to faculty using the
text for their course. Register at www.textbooks.elsevier.com and
search on "Theodoridis" to access resources for instructor. "
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