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Open Problems Spectral Dimensionality by Strange Harry (7 results)
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Open Problems in Spectral Dimensionality Reduction (SpringerBriefs in Computer Science)
Book 113 of 322: SpringerBriefs in Computer ScienceSeller: Ria Christie Collections, Uxbridge, United Kingdom
Seller rating 5 out of 5 stars
£ 49.34
£ 11.98 shipping
Ships from United Kingdom to U.S.A.Quantity: Over 20 available
Add to basketCondition: New. In.
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Paperback. Condition: Brand New. 2014 edition. 92 pages. 8.75x6.25x0.25 inches. In Stock.
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Taschenbuch. Condition: Neu. Open Problems in Spectral Dimensionality Reduction | Harry Strange (u. a.) | Taschenbuch | SpringerBriefs in Computer Science | ix | Englisch | 2014 | Springer | EAN 9783319039428 | Verantwortliche Person für die EU: Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg, juergen[dot]hartmann[at]springer[dot]com | Anbieter: preigu.
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Condition: Brand New. New. US edition. Print on demand title. Delivery takes 20-25 days.
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Open Problems in Spectral Dimensionality Reduction
Book 113 of 322: SpringerBriefs in Computer ScienceLanguage: English
Published by Springer International Publishing, 2014
ISBN 10: 3319039423 ISBN 13: 9783319039428
Condition: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Provides a clear and concise overview of spectral dimensionality reductionOffers uniquely practical knowledge without requiring a background in the areaSuggests interesting starting points for future research in this areaThe last few years.
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Open Problems in Spectral Dimensionality Reduction
Book 113 of 322: SpringerBriefs in Computer ScienceLanguage: English
Published by Springer, Palgrave Macmillan Jan 2014, 2014
ISBN 10: 3319039423 ISBN 13: 9783319039428
Seller: buchversandmimpf2000, Emtmannsberg, BAYE, Germany
Seller rating 5 out of 5 stars
Taschenbuch. Condition: Neu. This item is printed on demand - Print on Demand Titel. Neuware -The last few years have seen a great increase in the amount of data available to scientists, yet many of the techniques used to analyse this data cannot cope with such large datasets. Therefore, strategies need to be employed as a pre-processing step to reduce the number of objects or measurements whilst retaining important information. Spectral dimensionality reduction is one such tool for the data processing pipeline. Numerous algorithms and improvements have been proposed for the purpose of performing spectral dimensionality reduction, yet there is still no gold standard technique. This book provides a survey and reference aimed at advanced undergraduate and postgraduate students as well as researchers, scientists, and engineers in a wide range of disciplines. Dimensionality reduction has proven useful in a wide range of problem domains and so this book will be applicable to anyone with a solid grounding in statistics and computer science seeking to apply spectral dimensionality to their work.Springer-Verlag KG, Sachsenplatz 4-6, 1201 Wien 104 pp. Englisch.
