LEADER 05041nam 22007574a 450 001 9910824307003321 005 20200520144314.0 010 $a1-282-09675-3 010 $a9786612096754 010 $a0-262-25695-9 010 $a1-4237-7253-9 035 $a(CKB)1000000000461544 035 $a(EBL)3338671 035 $a(SSID)ssj0000209016 035 $a(PQKBManifestationID)11183794 035 $a(PQKBTitleCode)TC0000209016 035 $a(PQKBWorkID)10244426 035 $a(PQKB)10229362 035 $a(CaBNVSL)mat06267334 035 $a(IDAMS)0b000064818b4314 035 $a(IEEE)6267334 035 $a(OCoLC)68907209$z(OCoLC)78987607$z(OCoLC)182530751$z(OCoLC)473096469$z(OCoLC)488454745$z(OCoLC)568007491$z(OCoLC)606032834$z(OCoLC)648227163$z(OCoLC)654817487$z(OCoLC)681167521$z(OCoLC)722566384$z(OCoLC)728037419$z(OCoLC)806185765$z(OCoLC)888437564$z(OCoLC)961533509$z(OCoLC)962660136$z(OCoLC)988489574$z(OCoLC)991986007$z(OCoLC)994982647$z(OCoLC)1011994736$z(OCoLC)1037421521$z(OCoLC)1037908088$z(OCoLC)1038701247$z(OCoLC)1055345938$z(OCoLC)1081193106$z(OCoLC)1083553647 035 $a(OCoLC-P)68907209 035 $a(MaCbMITP)4908 035 $a(Au-PeEL)EBL3338671 035 $a(CaPaEBR)ebr10173735 035 $a(OCoLC)68907209 035 $a(MiAaPQ)EBC3338671 035 $a(EXLCZ)991000000000461544 100 $a20050802d2005 uy 0 101 0 $aeng 135 $aur|n|---||||| 181 $ctxt 182 $cc 183 $acr 200 00$aNearest-neighbor methods in learning and vision $etheory and practice /$fedited by Gregory Shakhnarovich, Trevor Darrell, Piotr Indyk 205 $a1st ed. 210 $aCambridge, Mass. $cMIT Press$dc2005 215 $a1 online resource (280 p.) 225 1 $aNeural information processing series 300 $a"... held in Whistler, British Columbia ... annual conference on Neural Information Processing Systems (NIPS) in December 2003"--Pref. 311 $a0-262-19547-X 320 $aIncludes bibliographical references and index. 327 $aContents; Series Foreword; Preface; 1 Introduction; I THEORY; 2 Nearest-Neighbor Searching and Metric Space Dimensions; 3 Locality-Sensitive Hashing Using Stable Distributions; II APPLICATIONS: LEARNING; 4 New Algorithms for Efficient High-Dimensional Nonparametric Classification; 5 Approximate Nearest Neighbor Regression in Very High Dimensions; 6 Learning Embeddings for Fast Approximate Nearest Neighbor Retrieval; III APPLICATIONS: VISION; 7 Parameter-Sensitive Hashing for Fast Pose Estimation; 8 Contour Matching Using Approximate Earth Mover's Distance 327 $a9 Adaptive Mean Shift Based Clustering in High Dimensions10 Object Recognition using Locality Sensitive Hashing of Shape Contexts; Contributors; Index 330 $aRegression and classification methods based on similarity of the input to stored examples have not been widely used in applications involving very large sets of high-dimensional data. Recent advances in computational geometry and machine learning, however, may alleviate the problems in using these methods on large data sets. This volume presents theoretical and practical discussions of nearest-neighbor (NN) methods in machine learning and examines computer vision as an application domain in which the benefit of these advanced methods is often dramatic. It brings together contributions from researchers in theory of computation, machine learning, and computer vision with the goals of bridging the gaps between disciplines and presenting state-of-the-art methods for emerging applications. The contributors focus on the importance of designing algorithms for NN search, and for the related classification, regression, and retrieval tasks, that remain efficient even as the number of points or the dimensionality of the data grows very large. The book begins with two theoretical chapters on computational geometry and then explores ways to make the NN approach practicable in machine learning applications where the dimensionality of the data and the size of the data sets make the naive methods for NN search prohibitively expensive. The final chapters describe successful applications of an NN algorithm, locality-sensitive hashing (LSH), to vision tasks. 410 0$aNeural information processing series. 606 $aAlgorithms$vCongresses 606 $aGeometry$xData processing$vCongresses 606 $aMachine learning$vCongresses 606 $aNearest neighbor analysis (Statistics)$vCongresses 615 0$aAlgorithms 615 0$aGeometry$xData processing 615 0$aMachine learning 615 0$aNearest neighbor analysis (Statistics) 676 $a006.3/1 701 $aDarrell$b Trevor$01614333 701 $aIndyk$b Piotr$0947726 701 $aShakhnarovich$b Gregory$01614332 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910824307003321 996 $aNearest-neighbor methods in learning and vision$93944119 997 $aUNINA