Introduction to Pattern Recognition

Introduction to Pattern Recognition: A Matlab Approach is an accompanying manual to Theodoridis/Koutroumbas' Pattern Recognition. It includes 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. This text is designed for electronic engineering, computer science, computer engineering, biomedical engineering and applied mathematics students taking graduate courses on pattern recognition and machine learning as well as R&D engineers and university researchers in image and signal processing/analyisis, and computer vision. Matlab code and descriptive summary of the most common methods and algorithms in Theodoridis/Koutroumbas, Pattern Recognition, Fourth Edition Solved examples in Matlab, including real-life data sets in imaging and audio recognition Available separately or at a special package price with the main text (ISBN for package: 978-0-12-374491-3)

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  • Author : Sergios Theodoridis
  • Publisher : Academic Press
  • Pages : 231 pages
  • ISBN : 9780080922751
  • Rating : 4/5 from 21 reviews
CLICK HERE TO GET THIS BOOKIntroduction to Pattern Recognition

Introduction to Pattern Recognition

Introduction to Pattern Recognition
  • Author : Sergios Theodoridis,Aggelos Pikrakis,Konstantinos Koutroumbas,Dionisis Cavouras
  • Publisher : Academic Press
  • Release : 03 March 2010
GET THIS BOOKIntroduction to Pattern Recognition

Introduction to Pattern Recognition: A Matlab Approach is an accompanying manual to Theodoridis/Koutroumbas' Pattern Recognition. It includes 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. This text is designed for electronic engineering, computer science, computer engineering, biomedical engineering and applied mathematics students taking graduate courses on pattern recognition and machine learning as well as R&D engineers

Introduction to Statistical Pattern Recognition

Introduction to Statistical Pattern Recognition
  • Author : Keinosuke Fukunaga
  • Publisher : Elsevier
  • Release : 22 October 2013
GET THIS BOOKIntroduction to Statistical Pattern Recognition

This completely revised second edition presents an introduction to statistical pattern recognition. Pattern recognition in general covers a wide range of problems: it is applied to engineering problems, such as character readers and wave form analysis as well as to brain modeling in biology and psychology. Statistical decision and estimation, which are the main subjects of this book, are regarded as fundamental to the study of pattern recognition. This book is appropriate as a text for introductory courses in pattern

Introduction to Pattern Recognition

Introduction to Pattern Recognition
  • Author : Menahem Friedman,Abraham Kandel
  • Publisher : World Scientific
  • Release : 24 June 1999
GET THIS BOOKIntroduction to Pattern Recognition

This book is an introduction to pattern recognition, meant for undergraduate and graduate students in computer science and related fields in science and technology. Most of the topics are accompanied by detailed algorithms and real world applications. In addition to statistical and structural approaches, novel topics such as fuzzy pattern recognition and pattern recognition via neural networks are also reviewed. Each topic is followed by several examples solved in detail. The only prerequisites for using this book are a one-semester

Introduction to Pattern Recognition

Introduction to Pattern Recognition
  • Author : Menahem Friedman,Abraham Kandel
  • Publisher : World Scientific Publishing Company
  • Release : 01 March 1999
GET THIS BOOKIntroduction to Pattern Recognition

This book is an introduction to pattern recognition, meant for undergraduate and graduate students in computer science and related fields in science and technology. Most of the topics are accompanied by detailed algorithms and real world applications. In addition to statistical and structural approaches, novel topics such as fuzzy pattern recognition and pattern recognition via neural networks are also reviewed. Each topic is followed by several examples solved in detail. The only prerequisites for using this book are a one-semester

Pattern Recognition

Pattern Recognition
  • Author : Jürgen Beyerer,Matthias Richter,Matthias Nagel
  • Publisher : Walter de Gruyter GmbH & Co KG
  • Release : 04 December 2017
GET THIS BOOKPattern Recognition

The book offers a thorough introduction to Pattern Recognition aimed at master and advanced bachelor students of engineering and the natural sciences. Besides classification - the heart of Pattern Recognition - special emphasis is put on features, their typology, their properties and their systematic construction. Additionally, general principles that govern Pattern Recognition are illustrated and explained in a comprehensible way. Rather than presenting a complete overview over the rapidly evolving field, the book is to clarifies the concepts so that

Pattern Recognition and Classification

Pattern Recognition and Classification
  • Author : Geoff Dougherty
  • Publisher : Springer Science & Business Media
  • Release : 28 October 2012
GET THIS BOOKPattern Recognition and Classification

The use of pattern recognition and classification is fundamental to many of the automated electronic systems in use today. However, despite the existence of a number of notable books in the field, the subject remains very challenging, especially for the beginner. Pattern Recognition and Classification presents a comprehensive introduction to the core concepts involved in automated pattern recognition. It is designed to be accessible to newcomers from varied backgrounds, but it will also be useful to researchers and professionals in

Pattern Recognition

Pattern Recognition
  • Author : Sergios Theodoridis,Konstantinos Koutroumbas
  • Publisher : Elsevier
  • Release : 15 May 2003
GET THIS BOOKPattern Recognition

Pattern recognition is a scientific discipline that is becoming increasingly important in the age of automation and information handling and retrieval. Patter Recognition, 2e covers the entire spectrum of pattern recognition applications, from image analysis to speech recognition and communications. This book presents cutting-edge material on neural networks, - a set of linked microprocessors that can form associations and uses pattern recognition to "learn" -and enhances student motivation by approaching pattern recognition from the designer's point of view. A direct

Applied Pattern Recognition

Applied Pattern Recognition
  • Author : Dietrich W.R. Paulus,Joachim Hornegger
  • Publisher : Springer
  • Release : 24 June 1998
GET THIS BOOKApplied Pattern Recognition

This book demonstrates the efficiency of the C++ programming language in the realm of pattern recognition and pattern analysis. It introduces the basics of software engineering, image and speech processing, als well as fundamental mathematical tools for pattern recognition. Step by step the C++ programming language is discribed. Each step is illustrated by examples based on challenging problems in image und speech processing. Particular emphasis is put on object-oriented programming and the implementation of efficient algorithms. The book proposes a

Statistical Pattern Recognition

Statistical Pattern Recognition
  • Author : Andrew R. Webb
  • Publisher : John Wiley & Sons
  • Release : 25 July 2003
GET THIS BOOKStatistical Pattern Recognition

Statistical pattern recognition is a very active area of study and research, which has seen many advances in recent years. New and emerging applications - such as data mining, web searching, multimedia data retrieval, face recognition, and cursive handwriting recognition - require robust and efficient pattern recognition techniques. Statistical decision making and estimation are regarded as fundamental to the study of pattern recognition. Statistical Pattern Recognition, Second Edition has been fully updated with new methods, applications and references. It provides

Introduction to Pattern Recognition and Machine Learning

Introduction to Pattern Recognition and Machine Learning
  • Author : M Narasimha Murty,V Susheela Devi
  • Publisher : World Scientific
  • Release : 22 April 2015
GET THIS BOOKIntroduction to Pattern Recognition and Machine Learning

This book adopts a detailed and methodological algorithmic approach to explain the concepts of pattern recognition. While the text provides a systematic account of its major topics such as pattern representation and nearest neighbour based classifiers, current topics — neural networks, support vector machines and decision trees — attributed to the recent vast progress in this field are also dealt with. Introduction to Pattern Recognition and Machine Learning will equip readers, especially senior computer science undergraduates, with a deeper understanding of the

Pattern Recognition

Pattern Recognition
  • Author : M. Narasimha Murty,V. Susheela Devi
  • Publisher : Springer Science & Business Media
  • Release : 25 May 2011
GET THIS BOOKPattern Recognition

Observing the environment and recognising patterns for the purpose of decision making is fundamental to human nature. This book deals with the scientific discipline that enables similar perception in machines through pattern recognition (PR), which has application in diverse technology areas. This book is an exposition of principal topics in PR using an algorithmic approach. It provides a thorough introduction to the concepts of PR and a systematic account of the major topics in PR besides reviewing the vast progress

Syntactic Pattern Recognition, Applications

Syntactic Pattern Recognition, Applications
  • Author : K.S. Fu
  • Publisher : Springer Science & Business Media
  • Release : 06 December 2012
GET THIS BOOKSyntactic Pattern Recognition, Applications

The many different mathematical techniques used to solve pattem recognition problems may be grouped into two general approaches: the decision-theoretic (or discriminant) approach and the syntactic (or structural) approach. In the decision-theoretic approach, aset of characteristic measurements, called features, are extracted from the pattems. Each pattem is represented by a feature vector, and the recognition of each pattem is usually made by partitioning the feature space. Applications of decision-theoretic approach indude character recognition, medical diagnosis, remote sensing, reliability and socio-economics.

Pattern Recognition and Machine Learning

Pattern Recognition and Machine Learning
  • Author : Christopher M. Bishop
  • Publisher : Springer
  • Release : 23 August 2016
GET THIS BOOKPattern Recognition and Machine Learning

This is the first textbook on pattern recognition to present the Bayesian viewpoint. The book presents approximate inference algorithms that permit fast approximate answers in situations where exact answers are not feasible. It uses graphical models to describe probability distributions when no other books apply graphical models to machine learning. No previous knowledge of pattern recognition or machine learning concepts is assumed. Familiarity with multivariate calculus and basic linear algebra is required, and some experience in the use of probabilities