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A Weighted Nearest Neighbor Algorithm for Learning with Symbolic Features
 Machine Learning
, 1993
"... In the past, nearest neighbor algorithms for learning from examples have worked best in domains in which all features had numeric values. In such domains, the examples can be treated as points and distance metrics can use standard definitions. In symbolic domains, a more sophisticated treatment of t ..."
Abstract

Cited by 309 (3 self)
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In the past, nearest neighbor algorithms for learning from examples have worked best in domains in which all features had numeric values. In such domains, the examples can be treated as points and distance metrics can use standard definitions. In symbolic domains, a more sophisticated treatment
Locally Adaptive Nearest Neighbor Algorithms
 Advances in Neural Information Processing Systems 6
, 1994
"... Four versions of a knearest neighbor algorithm with locally adaptive k are introduced and compared to the basic knearest neighbor algorithm (kNN). Locally adaptive kNN algorithms choose the value of k that should be used to classify a query by consulting the results of crossvalidation computatio ..."
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Cited by 19 (0 self)
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Four versions of a knearest neighbor algorithm with locally adaptive k are introduced and compared to the basic knearest neighbor algorithm (kNN). Locally adaptive kNN algorithms choose the value of k that should be used to classify a query by consulting the results of cross
Performance Analysis of Nearest Neighbor Algorithms
 in: 8th International Fall Workshop of Vision, Modeling, and Visalization
, 2003
"... There are many nearest neighbor algorithms tailormade for ICP, but most of them require special input data like range images or triangle meshes. We focus on efficient nearest neighbor algorithms that do not impose this limitation, and thus can also be used with 3D point sets generated by structure ..."
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There are many nearest neighbor algorithms tailormade for ICP, but most of them require special input data like range images or triangle meshes. We focus on efficient nearest neighbor algorithms that do not impose this limitation, and thus can also be used with 3D point sets generated by structure
Nearest neighbor queries.
 ACM SIGMOD Record,
, 1995
"... Abstract A frequently encountered type of query in Geographic Information Systems is to nd the k nearest neighbor objects to a given point in space. Processing such queries requires substantially di erent search algorithms than those for location or range queries. In this paper we present a n e cie ..."
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Cited by 592 (1 self)
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Abstract A frequently encountered type of query in Geographic Information Systems is to nd the k nearest neighbor objects to a given point in space. Processing such queries requires substantially di erent search algorithms than those for location or range queries. In this paper we present a n e
Fast approximate nearest neighbors with automatic algorithm configuration
 In VISAPP International Conference on Computer Vision Theory and Applications
, 2009
"... nearestneighbors search, randomized kdtrees, hierarchical kmeans tree, clustering. For many computer vision problems, the most time consuming component consists of nearest neighbor matching in highdimensional spaces. There are no known exact algorithms for solving these highdimensional problems ..."
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Cited by 455 (2 self)
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nearestneighbors search, randomized kdtrees, hierarchical kmeans tree, clustering. For many computer vision problems, the most time consuming component consists of nearest neighbor matching in highdimensional spaces. There are no known exact algorithms for solving these high
An investigation of practical approximate nearest neighbor algorithms
, 2004
"... This paper concerns approximate nearest neighbor searching algorithms, which have become increasingly important, especially in high dimensional perception areas such as computer vision, with dozens of publications in recent years. Much of this enthusiasm is due to a successful new approximate neares ..."
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Cited by 115 (4 self)
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This paper concerns approximate nearest neighbor searching algorithms, which have become increasingly important, especially in high dimensional perception areas such as computer vision, with dozens of publications in recent years. Much of this enthusiasm is due to a successful new approximate
An Optimal Algorithm for Approximate Nearest Neighbor Searching in Fixed Dimensions
 ACMSIAM SYMPOSIUM ON DISCRETE ALGORITHMS
, 1994
"... Consider a set S of n data points in real ddimensional space, R d , where distances are measured using any Minkowski metric. In nearest neighbor searching we preprocess S into a data structure, so that given any query point q 2 R d , the closest point of S to q can be reported quickly. Given any po ..."
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Cited by 984 (32 self)
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Consider a set S of n data points in real ddimensional space, R d , where distances are measured using any Minkowski metric. In nearest neighbor searching we preprocess S into a data structure, so that given any query point q 2 R d , the closest point of S to q can be reported quickly. Given any
A Nearest Neighbors Algorithm for Strings
, 2012
"... A randomized algorithm is presented for fast nearest neighbors search in libraries of strings. The algorithm is discussed in the context of one of the practical applications: aligning DNA reads to a reference genome. An implementation of the algorithm is shown to align about 10 6 reads per CPU minut ..."
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Cited by 1 (1 self)
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A randomized algorithm is presented for fast nearest neighbors search in libraries of strings. The algorithm is discussed in the context of one of the practical applications: aligning DNA reads to a reference genome. An implementation of the algorithm is shown to align about 10 6 reads per CPU
AverageCase Analysis of a Nearest Neighbor Algorithm
 PROCEEDINGS OF THE THIRTEENTH INTERNATIONAL JOINT CONFERENCE ON ARTIFICIAL INTELLIGENCE (PP. 889894). CHAMBERY
, 1993
"... In this paper we present an averagecase analysis of the nearest neighbor algorithm, a simple induction method that has been studied by many researchers. Our analysis assumes a conjunctive target concept, noisefree Boolean attributes, and a uniform distribution over the instance space. We calculate ..."
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Cited by 46 (6 self)
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In this paper we present an averagecase analysis of the nearest neighbor algorithm, a simple induction method that has been studied by many researchers. Our analysis assumes a conjunctive target concept, noisefree Boolean attributes, and a uniform distribution over the instance space. We
The Utility of Feature Weighting in NearestNeighbor Algorithms
 Proceedings of the Ninth European Conference on Machine Learning
, 1997
"... . Nearestneighbor algorithms are known to depend heavily on their distance metric. In this paper, we investigate the use of a weighted Euclidean metric in which the weight for each feature comes from a small set of options. We describe Diet, an algorithm that directs search through a space of discr ..."
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Cited by 39 (1 self)
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. Nearestneighbor algorithms are known to depend heavily on their distance metric. In this paper, we investigate the use of a weighted Euclidean metric in which the weight for each feature comes from a small set of options. We describe Diet, an algorithm that directs search through a space
Results 1  10
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