Supervised and Unsupervised Pattern Recognition Book [PDF] Download

Download the fantastic book titled Supervised and Unsupervised Pattern Recognition written by Evangelia Miche Tzanakou, available in its entirety in both PDF and EPUB formats for online reading. This page includes a concise summary, a preview of the book cover, and detailed information about "Supervised and Unsupervised Pattern Recognition", which was released on 19 December 2017. We suggest perusing the summary before initiating your download. This book is a top selection for enthusiasts of the Technology & Engineering genre.

Summary of Supervised and Unsupervised Pattern Recognition by Evangelia Miche Tzanakou PDF

There are many books on neural networks, some of which cover computational intelligence, but none that incorporate both feature extraction and computational intelligence, as Supervised and Unsupervised Pattern Recognition does. This volume describes the application of a novel, unsupervised pattern recognition scheme to the classification of various types of waveforms and images. This substantial collection of recent research begins with an introduction to Neural Networks, classifiers, and feature extraction methods. It then addresses unsupervised and fuzzy neural networks and their applications to handwritten character recognition and recognition of normal and abnormal visual evoked potentials. The third section deals with advanced neural network architectures-including modular design-and their applications to medicine and three-dimensional NN architecture simulating brain functions. The final section discusses general applications and simulations, such as the establishment of a brain-computer link, speaker identification, and face recognition. In the quickly changing field of computational intelligence, every discovery is significant. Supervised and Unsupervised Pattern Recognition gives you access to many notable findings in one convenient volume.


Detail About Supervised and Unsupervised Pattern Recognition PDF

  • Author : Evangelia Miche Tzanakou
  • Publisher : CRC Press
  • Genre : Technology & Engineering
  • Total Pages : 392 pages
  • ISBN : 1420049771
  • PDF File Size : 11,7 Mb
  • Language : English
  • Rating : 4/5 from 21 reviews

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Supervised and Unsupervised Pattern Recognition

Supervised and Unsupervised Pattern Recognition
  • Publisher : CRC Press
  • File Size : 41,7 Mb
  • Release Date : 19 December 2017
GET BOOK

There are many books on neural networks, some of which cover computational intelligence, but none that incorporate both feature extraction and computational intelligence, as Supervised and Unsupervised Pattern Recognition does.

Supervised and Unsupervised Pattern Recognition

Supervised and Unsupervised Pattern Recognition
  • Publisher : CRC Press
  • File Size : 40,7 Mb
  • Release Date : 19 December 2017
GET BOOK

There are many books on neural networks, some of which cover computational intelligence, but none that incorporate both feature extraction and computational intelligence, as Supervised and Unsupervised Pattern Recognition does.

Supervised and Unsupervised Pattern Recognition

Supervised and Unsupervised Pattern Recognition
  • Publisher : CRC Press
  • File Size : 37,6 Mb
  • Release Date : 19 December 2017
GET BOOK

There are many books on neural networks, some of which cover computational intelligence, but none that incorporate both feature extraction and computational intelligence, as Supervised and Unsupervised Pattern Recognition does.

Supervised and Unsupervised Pattern Recognition

Supervised and Unsupervised Pattern Recognition
  • Publisher : CRC Press
  • File Size : 31,7 Mb
  • Release Date : 28 December 1999
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There are many books on neural networks, some of which cover computational intelligence, but none that incorporate both feature extraction and computational intelligence, as Supervised and Unsupervised Pattern Recognition does.

Pattern Recognition and Classification

Pattern Recognition and Classification
  • Publisher : Springer Science & Business Media
  • File Size : 29,8 Mb
  • Release Date : 28 October 2012
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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

Supervised and Unsupervised Learning for Data Science

Supervised and Unsupervised Learning for Data Science
  • Publisher : Springer Nature
  • File Size : 44,8 Mb
  • Release Date : 04 September 2019
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This book covers the state of the art in learning algorithms with an inclusion of semi-supervised methods to provide a broad scope of clustering and classification solutions for big data

Sampling Techniques for Supervised or Unsupervised Tasks

Sampling Techniques for Supervised or Unsupervised Tasks
  • Publisher : Springer Nature
  • File Size : 46,6 Mb
  • Release Date : 26 October 2019
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This book describes in detail sampling techniques that can be used for unsupervised and supervised cases, with a focus on sampling techniques for machine learning algorithms. It covers theory and

Artificial Neural Networks in Pattern Recognition

Artificial Neural Networks in Pattern Recognition
  • Publisher : Springer
  • File Size : 24,5 Mb
  • Release Date : 16 April 2010
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Artificial Neural Networks in Pattern Recognition synthesizes the proceedings of the 4th IAPR TC3 Workshop, ANNPR 2010. Topics include supervised and unsupervised learning, feature selection, pattern recognition in signal and image

Pattern Recognition

Pattern Recognition
  • Publisher : Elsevier
  • File Size : 32,5 Mb
  • Release Date : 15 May 2003
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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

Face Image Analysis by Unsupervised Learning

Face Image Analysis by Unsupervised Learning
  • Publisher : Springer Science & Business Media
  • File Size : 29,9 Mb
  • Release Date : 30 June 2001
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Face Image Analysis by Unsupervised Learning explores adaptive approaches to image analysis. It draws upon principles of unsupervised learning and information theory to adapt processing to the immediate task environment.