L. Devroye, T.J. Wagner, "Nearest neighbor methods in discrimination," Handbook of Statistics, vol. 2, P.R. Krishnaiah, L.N. Kanal, eds., North-Holland, 1982.  Home/Search   Document Not in Database   Summary   Related Articles   Check

.... neighbor searching has applications in many areas, including knowledge discovery and data mining [FPSSU96] pattern recognition and classification [CH67, DH73] machine learning [CS93] data compression [GG92] multimedia databases [FSN 95] document retrieval [DDF 90] and statistics [DW82] There are many possible choices of the metric space. Throughout we will assume that the space is R , real d dimensional space, where distances are measured using any Minkowski Lm distance metric. For any integer m 1, the Lm distance between points p = p 1 ,p 2 , p d ) and q = q 1 ,q ....

L. Devroye and T. J. Wagner, Nearest neighbor methods in discrimination, Handbook of Statistics (P. R. Krishnaiah and L. N. Kanal, eds.), vol. 2, North-Holland, 1982.

....by the Fund for the Promotion of Research at the Technion. Email: rabani cs.technion.ac.il. 1 Introduction Problem definition and motivation. This paper is concerned with nearest neighbor search (NNS) a fundamental problem in computational geometry, with applications to a variety of areas [15, 20, 17, 33, 16, 22, 14, 32, 19, 23, 34, 8, 21]. The problem is defined as follows: In some vector space endowed with a distance function (typically a d dimensional Euclidean space) we are given a set of n points (called the database) Given any other point (called a query) we must find the closest point to it in the database. We have to ....

L. Devroye and T.J. Wagner. Nearest neighbor methods in discrimination. In Handbook of Statistics, Vol. 2, P.R. Krishnaiah and L.N. Kanal eds. North Holland, 1982.

.... is of major importance to a variety of applications; some examples are: data compression [20] databases and data mining [21] information retrieval [11, 16, 38] image and video databases [15, 17, 37, 42] machine learning [7] pattern recognition [9, 13] and, statistics and data analysis [12, 27]. Typically, the features of the objects of interest are represented as points in d and a distance metric is used to measure similarity of objects. The basic problem then is to perform indexing or similarity searching for query objects. The number of features (i.e. the dimensionality) ranges ....

L. Devroye and T.J. Wagner. Nearest neighbor methods in discrimination. Handbook of Statistics, vol. 2, P.R. Krishnaiah and L.N. Kanal, eds., North-Holland, 1982.

....applications, usually involving similarity searching. Some examples are: data compression [36] databases and data mining [13, 39] information retrieval [11, 21, 58] image and video databases [29, 31, 56, 61] machine learning [19] pattern recognition [20, 26] and, statistics and data analysis [22, 45]. Typically, the features of the objects of interest (documents, images, etc) are represented as points in d and a distance metric is used to measure (dis)similarity of objects. The basic problem then is to perform indexing or similarity searching for query objects. The number of features ....

L. Devroye and T.J. Wagner, Nearest neighbor methods in discrimination. In: Handbook of Statistics, vol. 2, P.R. Krishnaiah and L.N. Kanal, eds., North-Holland, 1982.

.... a specified database of points is a fundamental computational task that arises in a variety of application areas, including information retrieval [31, 32] data mining [19] pattern recognition [8, 14] machine learning [7] computer vision [4] data compression [17] and statistical data analysis [10]. In many of these applications the database points are represented as vectors in some high dimensional space. For example, latent semantic indexing is a recently proposed method for textual information retrieval [9] The semantic contents of documents, as well as the queries, are represented as ....

L. Devroye and T.J. Wagner. Nearest neighbor methods in discrimination. In Handbook of Statistics, Vol. 2, P.R. Krishnaiah and L.N. Kanal eds. North Holland, 1982.

....importance, in higher dimensions. The main application comes from information retrieval: the process of retrieving text and multimedia documents matching a specified query. Other instances of near neighbor search appear in algorithms for pattern recognition [6, 10] statistics and data analysis [26, 8], machine learning [5] data compression [12] data mining [13] and image analysis [20] In the case of text retrieval, vector space methods [3, 28] map each document into a point in high dimensional space. Sometimes, statistical techniques such as principal components analysis [14] latent ....

L. Devroye and T.J. Wagner, "Nearest neighbor methods in discrimination," Handbook of Statistics, vol. 2, P.R. Krishnaiah, L.N. Kanal, eds., North-Holland, 1982.

....1 Introduction The nearest neighbor problem is central to a wide range of areas in which computational techniques are applied. Nearest neighbor based methods appear, for example, in algorithms for information retrieval [37, 38, 8, 15] pattern recognition [14, 18] statistics and data analysis [35, 16], data compression [27] and multimedia databases [36, 23, 40] The pervasiveness of the problem arises in large part because of its effectiveness as a general purpose means of indexing and comparing data: one represents objects as points in a high dimensional metric space, and then uses ....

L. Devroye, T.J. Wagner, "Nearest neighbor methods in discrimination," Handbook of Statistics, vol. 2, P.R. Krishnaiah, L.N. Kanal, eds., North-Holland, 1982.

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L. Devroye, T.J. Wagner, "Nearest neighbor methods in discrimination," Handbook of Statistics, vol. 2, P.R. Krishnaiah, L.N. Kanal, eds., North-Holland, 1982.

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L. Devroye and T.J. Wagner. Nearest neighbor methods in discrimination. In Handbook of Statistics, Vol. 2, P.R. Krishnaiah and L.N. Kanal eds. North Holland, 1982. 15

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L. Devroye and T.J. Wagner. Nearest neighbor methods in discrimination. Handbook of Statistics, volume 2, P.R. Krishnaiah and L.N. Kanal, editors, North-Holland, 1982.

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