Results 1  10
of
118,261
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 ..."
Abstract

Cited by 448 (2 self)
 Add to MetaCart
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
Two Algorithms for NearestNeighbor Search in High Dimensions
, 1997
"... Representing data as points in a highdimensional space, so as to use geometric methods for indexing, is an algorithmic technique with a wide array of uses. It is central to a number of areas such as information retrieval, pattern recognition, and statistical data analysis; many of the problems aris ..."
Abstract

Cited by 201 (0 self)
 Add to MetaCart
arising in these applications can involve several hundred or several thousand dimensions. We consider the nearestneighbor problem for ddimensional Euclidean space: we wish to preprocess a database of n points so that given a query point, one can efficiently determine its nearest neighbors
When Is "Nearest Neighbor" Meaningful?
 In Int. Conf. on Database Theory
, 1999
"... . We explore the effect of dimensionality on the "nearest neighbor " problem. We show that under a broad set of conditions (much broader than independent and identically distributed dimensions), as dimensionality increases, the distance to the nearest data point approaches the distance ..."
Abstract

Cited by 402 (1 self)
 Add to MetaCart
. We explore the effect of dimensionality on the "nearest neighbor " problem. We show that under a broad set of conditions (much broader than independent and identically distributed dimensions), as dimensionality increases, the distance to the nearest data point approaches
Discriminant Adaptive Nearest Neighbor Classification
, 1994
"... Nearest neighbor classification expects the class conditional probabilities to be locally constant, and suffers from bias in high dimensions. We propose a locally adaptive form of nearest neighbor classification to try to ameliorate this curse of dimensionality. We use a local linear discriminant an ..."
Abstract

Cited by 322 (1 self)
 Add to MetaCart
Nearest neighbor classification expects the class conditional probabilities to be locally constant, and suffers from bias in high dimensions. We propose a locally adaptive form of nearest neighbor classification to try to ameliorate this curse of dimensionality. We use a local linear discriminant
An experimental comparison of the nearestneighbor and nearesthyperrectangle algorithms
 Machine Learning
, 1995
"... Abstract. Algorithms based on Nested Generalized Exemplar (NGE) theory (Salzberg, 1991) classify new data points by computing their distance to the nearest "generalized exemplar " (i.e., either a point or an axisparallel rectangle). They combine the distancebased character of nearest nei ..."
Abstract

Cited by 107 (4 self)
 Add to MetaCart
neighbor (NN) classifiers with the axisparallel rectangle representation employed in many rulelearning systems. An implementation of NGE was compared to the knearest neighbor (kNN) algorithm in I 1 domains and found to be significantly inferior to kNN in 9 of them. Several modifications of NGE were
A Hybrid NearestNeighbor and NearestHyperrectangle Algorithm
 in the Proceedings of the 7th European Conference on Machine Learning
, 1994
"... . Algorithms based on Nested Generalized Exemplar (NGE) theory [10] classify new data points by computing their distance to the nearest "generalized exemplar" (i.e. an axisparallel multidimensional rectangle). An improved version of NGE, called BNGE, was previously shown to perform compar ..."
Abstract

Cited by 38 (1 self)
 Add to MetaCart
comparably to the Nearest Neighbor algorithm. Advantages of the NGE approach include compact representation of the training data and fast training and classification. A hybrid method that combines BNGE and the kNearest Neighbor algorithm, called KBNGE, is introduced for improved classification accuracy
Nearestneighbor Queries in Probabilistic Graphs
"... Abstract — Large probabilistic graphs arise in various domains spanning from social networks to biological and communication networks. An important query in these graphs is the k nearestneighbor query, which involves finding and reporting the k closest nodes to a specific node. This query assumes th ..."
Abstract

Cited by 3 (0 self)
 Add to MetaCart
are computationally intractable to compute exactly. Thus, in order to process nearestneighbor queries, we resort to Monte Carlo sampling and exploit novel graphtransformation ideas and pruning opportunities. In our extensive experimental analysis, we explore the tradeoffs of our approximation algorithms
CHOICE OF NEIGHBOR ORDER IN NEARESTNEIGHBOR CLASSIFICATION
, 810
"... The kthnearest neighbor rule is arguably the simplest and most intuitively appealing nonparametric classification procedure. However, application of this method is inhibited by lack of knowledge about its properties, in particular, about the manner in which it is influenced by the value of k; and b ..."
Abstract

Cited by 16 (1 self)
 Add to MetaCart
The kthnearest neighbor rule is arguably the simplest and most intuitively appealing nonparametric classification procedure. However, application of this method is inhibited by lack of knowledge about its properties, in particular, about the manner in which it is influenced by the value of k
Nearestneighbor classification for facies delineation
"... [1] Geostatistics has become the dominant tool for probabilistic estimation of properties of heterogeneous formations at points where data are not available. Ordinary kriging, the starting point in the development of other geostatistical techniques, has a number of serious limitations, chief among w ..."
Abstract
 Add to MetaCart
that is adequate for the task of facies delineation. Guided by the principle of parsimony, we identify nearestneighbor classification (NNC) as a viable alternative to geostatistics among deterministic techniques. We demonstrate that when used for the purpose of facies delineation, NNC, which has no fitting
Results 1  10
of
118,261