Abstract. Choosing which fast Nearest Neighbour search algorithm to use depends on the task we face. Usually kd-tree search algorithm is selected when the similarity function is the Euclidean or the Manhattan distances. Generic fast search algorithms (algorithms that works with any distance function) are only used when there is not specific fast search algorithms for the involved distance function. In this work we show that in real data problems generic search algo-rithms (i.e. MDF-tree) can be faster that specific ones (i.e. kd-tree).
Some fast nearest neighbor search (NNS) algorithms using metric properties have appeared in the last...
The long-standing problem of efficient nearest-neighbor (NN) search has ubiqui-tous applications ran...
Abstract The nearest neighbour (NN) classification rule is usuallychosen in a large number of patter...
Choosing which fast Nearest Neighbour search algorithm to use depends on the task we face. Usually k...
A simplified k nearest neighbour (knn) search for the R-tree family is proposed in this paper. This ...
For an increasing number of modern database applica-tions, efficient support of similarity search be...
International audienceWe compare the performance of three nearest neighbor search algorithms: the Or...
The thesis describes the basic concept of the K-nearest neighbors algorithm and its connection with ...
In this paper, we present a fast and versatile algorithm which can rapidly perform a variety of near...
Affordable, fast computers with large memories have lessened the demand for program efficiency, but ...
Similarity search problems in high-dimensional data arise in many areas of computer science such as ...
Nearest neighbor search is a basic primitive method used for machine learning and information retrie...
English: Nearest neighbour search is the one of the most simple and used technique in Pattern Recogn...
In many computer vision problems, answering the nearest neighbor queries efficiently, especially in ...
Data structures such as k-D trees and hierarchical k-means trees perform very well in approximate k ...
Some fast nearest neighbor search (NNS) algorithms using metric properties have appeared in the last...
The long-standing problem of efficient nearest-neighbor (NN) search has ubiqui-tous applications ran...
Abstract The nearest neighbour (NN) classification rule is usuallychosen in a large number of patter...
Choosing which fast Nearest Neighbour search algorithm to use depends on the task we face. Usually k...
A simplified k nearest neighbour (knn) search for the R-tree family is proposed in this paper. This ...
For an increasing number of modern database applica-tions, efficient support of similarity search be...
International audienceWe compare the performance of three nearest neighbor search algorithms: the Or...
The thesis describes the basic concept of the K-nearest neighbors algorithm and its connection with ...
In this paper, we present a fast and versatile algorithm which can rapidly perform a variety of near...
Affordable, fast computers with large memories have lessened the demand for program efficiency, but ...
Similarity search problems in high-dimensional data arise in many areas of computer science such as ...
Nearest neighbor search is a basic primitive method used for machine learning and information retrie...
English: Nearest neighbour search is the one of the most simple and used technique in Pattern Recogn...
In many computer vision problems, answering the nearest neighbor queries efficiently, especially in ...
Data structures such as k-D trees and hierarchical k-means trees perform very well in approximate k ...
Some fast nearest neighbor search (NNS) algorithms using metric properties have appeared in the last...
The long-standing problem of efficient nearest-neighbor (NN) search has ubiqui-tous applications ran...
Abstract The nearest neighbour (NN) classification rule is usuallychosen in a large number of patter...