Pattern Recognition

Pattern Recognition 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 eng

  • Title: Pattern Recognition
  • Author: Sergios Theodoridis Konstantinos Koutroumbas
  • ISBN: 9781597492720
  • Page: 246
  • Format: Hardcover
  • 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.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 worked examples to give greater understanding of the various methods and techniques Many 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 worked examples to give greater understanding of the various methods and techniques Many 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 textbooks.elsevier and search on Theodoridis to access resources for instructor.

    • ✓ Pattern Recognition || ☆ PDF Download by ✓ Sergios Theodoridis Konstantinos Koutroumbas
      246 Sergios Theodoridis Konstantinos Koutroumbas
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      Posted by:Sergios Theodoridis Konstantinos Koutroumbas
      Published :2018-04-04T15:43:12+00:00

    1 thought on “Pattern Recognition”

    1. If I were to synopsize my experience with this book, it would be "hard to read".It covers a wide range of topics and you can get an idea of algorithms from all across the Pattern Recognition and Machine Learning spectrum - even though it is a bit outdated and lacking in some concepts (like Neural Networks).The problem is that it is very taxing to get from "I have an idea what this is about" to "I understand what this is about". Wall-o-texts, cumbersome notation, a lack of algorithm analysis all [...]

    2. I have not read other ML-related books, but I found this one very practical for basics of understanding of ML, Neural Networks, and Pattern Recognitions.

    3. Overall it was decent way to learn about pattern recognition, however I felt some of the concepts were hidden behind a wall of text that did not really add to my understanding.

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