Unsupervised Learning: A Dynamic Approach (IEEE Press Series on Computational Intelligence) - Hardcover

Kyan, Matthew; Muneesawang, Paisarn; Jarrah, Kambiz; Guan, Ling

Hardcover

ISBN 10: 0470278331 ISBN 13: 9780470278338

Publisher: Wiley-IEEE Press, 2014

Synopsis

A new approach to unsupervised learning

Evolving technologies have brought about an explosion of information in recent years, but the question of how such information might be effectively harvested, archived, and analyzed remains a monumental challenge―for the processing of such information is often fraught with the need for conceptual interpretation: a relatively simple task for humans, yet an arduous one for computers.

Inspired by the relative success of existing popular research on self-organizing neural networks for data clustering and feature extraction, Unsupervised Learning: A Dynamic Approach presents information within the family of generative, self-organizing maps, such as the self-organizing tree map (SOTM) and the more advanced self-organizing hierarchical variance map (SOHVM). It covers a series of pertinent, real-world applications with regard to the processing of multimedia data―from its role in generic image processing techniques, such as the automated modeling and removal of impulse noise in digital images, to problems in digital asset management and its various roles in feature extraction, visual enhancement, segmentation, and analysis of microbiological image data.

Self-organization concepts and applications discussed include:

• Distance metrics for unsupervised clustering
• Synaptic self-amplification and competition
• Image retrieval
• Impulse noise removal
• Microbiological image analysis

Unsupervised Learning: A Dynamic Approach introduces a new family of unsupervised algorithms that have a basis in self-organization, making it an invaluable resource for researchers, engineers, and scientists who want to create systems that effectively model oppressive volumes of data with little or no user intervention.

About this title

About the Author

MATTHEW KYAN received his Ph.D. in Electrical Engineering in 2007 from the University of Sydney, Australia, winning the Siemens National Prize for Innovation for his work with 3-D confocal imaging. He is currently an Assistant Professor at Ryerson University, Toronto, Canada.

PAISARN MUNEESAWANG received his Ph.D. from the school of Electrical and Information Engineering at the University of Sydney in 2002. He is currently an Associate Professor at Naresuan University.

KAMBIZ JARRAH received his B.Eng. (with honors) in 2004 and M.A.Sc. in 2006, both in Electrical Engineering, from Ryerson University.

LING GUAN is a Canada Research Chair in Multimedia and Computer Technology and a Professor in Electrical and Computer Engineering at Ryerson University, Canada.

From the Back Cover

A new approach to unsupervised learning

Evolving technologies have brought about an explosion of information in recent years, but the question of how such information might be effectively harvested, archived, and analyzed remains a monumental challenge―for the processing of such information is often fraught with the need for conceptual interpretation: a relatively simple task for humans, yet an arduous one for computers.

Inspired by the relative success of existing popular research on self-organizing neural networks for data clustering and feature extraction, Unsupervised Learning: A Dynamic Approach presents information within the family of generative, self-organizing maps, such as the self-organizing tree map (SOTM) and the more advanced self-organizing hierarchical variance map (SOHVM). It covers a series of pertinent, real-world applications with regard to the processing of multimedia data―from its role in generic image processing techniques, such as the automated modeling and removal of impulse noise in digital images, to problems in digital asset management and its various roles in feature extraction, visual enhancement, segmentation, and analysis of microbiological image data.

Self-organization concepts and applications discussed include:

• Distance metrics for unsupervised clustering
• Synaptic self-amplification and competition
• Image retrieval
• Impulse noise removal
• Microbiological image analysis

Unsupervised Learning: A Dynamic Approach introduces a new family of unsupervised algorithms that have a basis in self-organization, making it an invaluable resource for researchers, engineers, and scientists who want to create systems that effectively model oppressive volumes of data with little or no user intervention.

From the Inside Flap

A new approach to unsupervised learning

Evolving technologies have brought about an explosion of information in recent years, but the question of how such information might be effectively harvested, archived, and analyzed remains a monumental challenge—for the processing of such information is often fraught with the need for conceptual interpretation: a relatively simple task for humans, yet an arduous one for computers.

Inspired by the relative success of existing popular research on self-organizing neural networks for data clustering and feature extraction, Unsupervised Learning: A Dynamic Approach presents information within the family of generative, self-organizing maps, such as the self-organizing tree map (SOTM) and the more advanced self-organizing hierarchical variance map (SOHVM). It covers a series of pertinent, real-world applications with regard to the processing of multimedia data—from its role in generic image processing techniques, such as the automated modeling and removal of impulse noise in digital images, to problems in digital asset management and its various roles in feature extraction, visual enhancement, segmentation, and analysis of microbiological image data.

Self-organization concepts and applications discussed include:

• Distance metrics for unsupervised clustering
• Synaptic self-amplification and competition
• Image retrieval
• Impulse noise removal
• Microbiological image analysis

Unsupervised Learning: A Dynamic Approach introduces a new family of unsupervised algorithms that have a basis in self-organization, making it an invaluable resource for researchers, engineers, and scientists who want to create systems that effectively model oppressive volumes of data with little or no user intervention.

Edition details

• Publisher: Wiley-IEEE Press
• Publication date: 2014
• Language: English
• ISBN 10: 0470278331
• ISBN 13: 9780470278338
• Binding: Hardcover
• Edition number: 1
• Number of pages: 288