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 manyworked examples to give greater understanding of the various methods and techniquesManydiagrams included now in two color to provide greater insight through visual presentationMatlab code of the most common methods are given at the end of each chapterMore 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 algorithmsAn 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 ISBNThoroughly developed to include manyworked examples to give greater understanding of the various methods and techniques Manydiagrams 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 ISBNPackage ISBNLatest 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 textbookselsevier and search on Theodoridis to access resources for instructor


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