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001385552 24500 $$aNearest-neighbor methods in learning and vision :$$btheory and practice /$$cedited by Gregory Shakhnarovich, Trevor Darrell, Piotr Indyk.
001385552 260__ $$aCambridge, Mass. :$$bMIT Press,$$c©2005.
001385552 300__ $$a1 online resource (vi, 252 pages) :$$billustrations.
001385552 336__ $$atext$$btxt$$2rdacontent
001385552 337__ $$acomputer$$bc$$2rdamedia
001385552 338__ $$aonline resource$$bcr$$2rdacarrier
001385552 4901_ $$aNeural information processing series
001385552 500__ $$a" ... held in Whistler, British Columbia ... annual conference on Neural Information Processing Systems (NIPS) in December 2003"--Preface.
001385552 506__ $$aAccess limited to authorized users.
001385552 520__ $$aRegression and classification methods based on similarity of the input to stored examples have not been widely used in applications involving very large sets of high-dimensional data. Recent advances in computational geometry and machine learning, however, may alleviate the problems in using these methods on large data sets. This volume presents theoretical and practical discussions of nearest-neighbor (NN) methods in machine learning and examines computer vision as an application domain in which the benefit of these advanced methods is often dramatic. It brings together contributions from researchers in theory of computation, machine learning, and computer vision with the goals of bridging the gaps between disciplines and presenting state-of-the-art methods for emerging applications. The contributors focus on the importance of designing algorithms for NN search, and for the related classification, regression, and retrieval tasks, that remain efficient even as the number of points or the dimensionality of the data grows very large. The book begins with two theoretical chapters on computational geometry and then explores ways to make the NN approach practicable in machine learning applications where the dimensionality of the data and the size of the data sets make the naive methods for NN search prohibitively expensive. The final chapters describe successful applications of an NN algorithm, locality-sensitive hashing (LSH), to vision tasks.
001385552 588__ $$aOCLC-licensed vendor bibliographic record.
001385552 650_0 $$aNearest neighbor analysis (Statistics)$$vCongresses.
001385552 650_0 $$aMachine learning$$vCongresses.
001385552 650_0 $$aAlgorithms$$vCongresses.
001385552 650_0 $$aGeometry$$xData processing$$vCongresses.
001385552 653__ $$aCOMPUTER SCIENCE/Machine Learning & Neural Networks
001385552 655_0 $$aElectronic books
001385552 7001_ $$aShakhnarovich, Gregory.
001385552 7001_ $$aDarrell, Trevor.
001385552 7001_ $$aIndyk, Piotr.
001385552 852__ $$bebk
001385552 85640 $$3MIT Press$$uhttps://univsouthin.idm.oclc.org/login?url=https://doi.org/10.7551/mitpress/4908.001.0001?locatt=mode:legacy$$zOnline Access through The MIT Press Direct
001385552 85642 $$3OCLC metadata license agreement$$uhttp://www.oclc.org/content/dam/oclc/forms/terms/vbrl-201703.pdf
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