eBook, ePUB

Supervised and Unsupervised Pattern Recognition (eBook, ePUB)

Feature Extraction and Computational Intelligence

Redaktion: Tzanakou, Evangelia Miche

Beschreibung

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.

Technische Details

• Geräte: eReader
• mit Kopierschutz
• Größe: 13.06MB

Barrierefreiheit

• Text-to-Speech
• E-Mail des Verlags für Barrierefreiheitsfragen: [e-mail]
• Seitennummerierung folgt dem gedruckten Werk
• Mathematische Inhalte in MathML für assistive Technologien
• Text und Medien in logischer Lesereihenfolge angeordnet

Autor

Evangelia Miche Tzanakou

Produktdetails

• Verlag: Taylor & Francis eBooks
• Seitenzahl: 392
• Erscheinungstermin: 19. Dezember 2017
• Englisch
• ISBN-13: 9781351835558
• Artikelnr.: 50529380

Rezensionen

"This book is an excellent source of knowledge of state-of-the-art feature extraction...Supervised and unsupervised learning and training schemes are notable finds...Exciting applications of signal and image analysis and recognition...This book provides in-depth guidance and inspiring ideas to new applications of signal and image analysis and recognition."
--Tonglei Li, Ph.D., Purdue University, School of Pharmacy
"...great efforts have been made in a number of communities to explore solutions to pattern recognition problems...this book describes their efforts made over ten researchers in the Neuroelectric and Neurocomputing Laboratories at Rutgers University. Along with concise introductory materials in pattern recognition, this

volume presents several applications of supervised and unsupervised schemes to the classification of various types of signals and images...Unlike other books in neural networks, this book gives an emphasis on feature extraction as well, which provides a systematic way to deal with pattern recognition problems in terms of neural networks and computational intelligence...it is worth noting that each chapter contains an extensive bibliography that provides a reliable list of good references. We believe that readers will find this list very useful to understand the materials in the book and cautious beginners in the related fields might benefit from this list as well...helpful to a broad audience of graduate students, researchers, practicing engineers and professionals in computer and information science, electrical engineering, and biomedical informatics...this book reflects the long-term continuous endeavors of a research group for conducting innovatory researches, which could provide some useful hints to those novices in related fields...pioneering volume...welcomed by all interested in the fields of pattern recognition and computational intelligence...the editor's serious attempt to address the aforementioned issue must be welcomed by all interested in the fields of pattern recognition and computational intelligence and, therefore, this book deserves all credit."
--Ke Chen, National Laboratory of Machine Perception and The Center for Information Science, Peking University, Beijing, China
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