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Unsupervised Learning - A Dynamic Approach (Hardcover)
Loot Price: R3,216
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Unsupervised Learning - A Dynamic Approach (Hardcover)
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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.
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