Nearest-Neighbor Methods in Learning and Vision Theory and Practice

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 m...

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Hlavní autoři: Shakhnarovich, Gregory, Darrell, Trevor, Indyk, Piotr
Médium: E-kniha Kniha
Jazyk:angličtina
Vydáno: Cambridge, Mass MIT Press 2006
The MIT Press
Vydání:1
Edice:Neural Information Processing series
Témata:
Algorithms
Algorithms > Congresses
Computer Science
Computing and Processing
Congresses
Data processing
Geometry
Geometry > Data processing > Congresses
Machine learning
Machine Learning & Neural Networks
Machine learning > Congresses
Nearest neighbor analysis (Statistics)
Nearest neighbor analysis (Statistics) > Congresses
ISBN:9780262195478, 026219547X
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Shrnutí: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.
Bibliografie:Includes bibliographical references and index
Available also in a print ed.
Mode of access: Internet via World Wide Web.
Title from title screen.
ISBN:9780262195478
026219547X
DOI:10.7551/mitpress/4908.001.0001