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Unsupervised Learning: A Dynamic Approach by Matthew Kyan (English) Hardcover Bo
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- Compralo SubitoUnsupervised Learning: A Dynamic Approach by Matthew Kyan (English) Hardcover Bo
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Numero oggetto eBay:388680534715
Specifiche dell'oggetto
- Condizione
- ISBN-13
- 9780470278338
- Book Title
- Unsupervised Learning
- ISBN
- 9780470278338
Informazioni su questo prodotto
Product Identifiers
Publisher
Wiley & Sons, Incorporated, John
ISBN-10
0470278331
ISBN-13
9780470278338
eBay Product ID (ePID)
72344656
Product Key Features
Number of Pages
288 Pages
Language
English
Publication Name
Unsupervised Learning : a Dynamic Approach
Subject
Intelligence (Ai) & Semantics, System Theory
Publication Year
2014
Type
Textbook
Subject Area
Computers, Science
Series
IEEE Press Series on Computational Intelligence Ser.
Format
Hardcover
Dimensions
Item Height
0.9 in
Item Weight
20.7 Oz
Item Length
9.6 in
Item Width
6.4 in
Additional Product Features
Intended Audience
Scholarly & Professional
LCCN
2013-046024
Series Volume Number
11
Illustrated
Yes
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., 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., To aid in intelligent data mining, this book introduces a new family of unsupervised algorithms that have a basis in self-organization, yet are free from many of the constraints typical of other well known self-organizing architectures., A new approach to unsupervised learningEvolving 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 clusteringSynaptic self-amplification and competitionImage retrievalImpulse noise removalMicrobiological 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.
LC Classification Number
Q325
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grandeagleretail
98,2% di Feedback positivi•2,8 milioni oggetti venduti
Registrato come venditore professionale
Feedback sul venditore (1.056.366)
- c***a (121)- Feedback lasciato dall'acquirente.Mese scorsoAcquisto verificatoThe seller was very responsive and answered me on a timely matter. The product itself came in its packaging and was new, not used at all. The packaging was not beat up or anything, safely delivered to my mailbox. No mix ups and zero stress with delivery. The price for the product is completely understandable for the product. I really appreciate the seller and I am very happy to have purchased through this seller. Completely trustable!
- m***4 (1604)- Feedback lasciato dall'acquirente.Mese scorsoAcquisto verificatoLeaving positive feedback because 1) item was packed well & arrived as described 2) seller did give partial refund when subsequent price dropped below org purchase price. 3) communication was quick However, there was a downside to this transaction -item listed as in-stock but ended up waiting nearly a month for them to get it from their distributer then ship it to me (bought June 29th, arrived around July 21). Auction said 12-15 days. Better clarity would have prevented lot of frustration
- w***i (875)- Feedback lasciato dall'acquirente.Ultimi 6 mesiAcquisto verificatoAbsolutely Wonderful Seller!! Terrific Item As Described!!! Great Service and Communication!! Shipped In Waterproof Packaging!! I Received Item In About One Week!! Very Pleased With Seller! I Will Buy From This Seller Again!!