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Nearest-Neighbor Methods in Learning and Vision : Theory and Practice

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Title: Nearest-Neighbor Methods in Learning and Vision : Theory and Practice
Description: Advances in computational geometry and machine learning that offer new methods for search, regression, and classification with large amounts of high-dimensional data. 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.
Authors: Gregory Shakhnarovich, Trevor Darrell, Piotr Indyk
Resource Type: eBook.
Subjects: Geometry--Data processing--Congresses, Artificial intelligence, Nearest neighbor analysis (Statistics)--Congresses, Machine learning--Congresses, Algorithms--Congresses
Categories: COMPUTERS / Computer Science, COMPUTERS / Artificial Intelligence / General, HEALTH & FITNESS / Vision
Database: eBook Collection (EBSCOhost)
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  Data: Advances in computational geometry and machine learning that offer new methods for search, regression, and classification with large amounts of high-dimensional data. 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.
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        Type: prePub
    Languages:
      – Code: eng
        Text: English
    Subjects:
      – SubjectFull: Geometry--Data processing--Congresses
        Type: general
      – SubjectFull: Artificial intelligence
        Type: general
      – SubjectFull: Nearest neighbor analysis (Statistics)--Congresses
        Type: general
      – SubjectFull: Machine learning--Congresses
        Type: general
      – SubjectFull: Algorithms--Congresses
        Type: general
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      – TitleFull: Nearest-Neighbor Methods in Learning and Vision : Theory and Practice
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            NameFull: Gregory Shakhnarovich
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            NameFull: Trevor Darrell
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            NameFull: Piotr Indyk
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            NameFull: Gregory Shakhnarovich
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            NameFull: Trevor Darrell
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          Dates:
            – D: 01
              M: 01
              Type: published
              Y: 2005
            – D: 04
              M: 02
              Type: profile
              Y: 2014
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            – Type: isbn-print
              Value: 9780262195478
            – Type: isbn-electronic
              Value: 9780262256957
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            – TitleFull: Nearest-Neighbor Methods in Learning and Vision : Theory and Practice
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