Nearest-Neighbor Methods in Learning and Vision: Theory and Practice (Neural Information Processing series) - nuovo libro
ISBN: 0262256959
Printed Access Code, [EAN: 9780262256957], The MIT Press, The MIT Press, Book, [PU: The MIT Press], The MIT Press, Regression and classification methods based on similarity of the input t… Altro …
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2019, ISBN: 9780262256957
Theory and Practice, eBooks, eBook Download (PDF), [PU: MIT Press Ltd], MIT Press Ltd, 2019
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2006, ISBN: 9780262256957
Theory and Practice, Online Resource, Software, [PU: MIT Press]
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Nearest-Neighbor Methods in Learning and Vision: Theory and Practice (Neural Information Processing series) - nuovo libro
ISBN: 0262256959
Printed Access Code, [EAN: 9780262256957], The MIT Press, The MIT Press, Book, [PU: The MIT Press], The MIT Press, Regression and classification methods based on similarity of the input t… Altro …
Trevor Darrell; Massachusetts Institute of Technology) Indyk Piotr (Professor; Gregory Shakhnarovich:
Nearest-Neighbor Methods in Learning and Vision - nuovo libro2019, ISBN: 9780262256957
Theory and Practice, eBooks, eBook Download (PDF), [PU: MIT Press Ltd], MIT Press Ltd, 2019
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Regression and classification methods based on similarity of the input to stored examples have not been widely used in applications involving very large sets of high-dimensional data. Recent advances in computational geometry and machine learning, however, may alleviate the problems in using these methods on large data sets. This volume presents theoretical and practical discussions of nearest-neighbor (NN) methods in machine learning and examines computer vision as an application domain in which the benefit of these advanced methods is often dramatic. It brings together contributions from researchers in theory of computation, machine learning, and computer vision with the goals of bridging the gaps between disciplines and presenting state-of-the-art methods for emerging applications.The contributors focus on the importance of designing algorithms for NN search, and for the related classification, regression, and retrieval tasks, that remain efficient even as the number of points or the dimensionality of the data grows very large. The book begins with two theoretical chapters on computational geometry and then explores ways to make the NN approach practicable in machine learning applications where the dimensionality of the data and the size of the data sets make the naïve methods for NN search prohibitively expensive. The final chapters describe successful applications of an NN algorithm, locality-sensitive hashing (LSH), to vision tasks.
Informazioni dettagliate del libro - Nearest-Neighbor Methods in Learning and Vision
EAN (ISBN-13): 9780262256957
ISBN (ISBN-10): 0262256959
Anno di pubblicazione: 2006
Editore: MIT Press Ltd
Libro nella banca dati dal 2018-07-03T13:50:40+02:00 (Zurich)
Pagina di dettaglio ultima modifica in 2022-05-31T21:06:55+02:00 (Zurich)
ISBN/EAN: 0262256959
ISBN - Stili di scrittura alternativi:
0-262-25695-9, 978-0-262-25695-7
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