Abstract
The k-nearest-neighbour (k-NN) search algorithm is widely used in pattern classification tasks. A large set of fast k-NN search algorithms have been developed in order to obtain lower error rates. Most of them are extensions of fast NN search algorithms where the condition of finding exactly the k nearest neighbours is imposed. All these algorithms calculate a number of distances that increases with k. Also, a vector-space representation is usually needed in these algorithms. If the condition of finding exactly the k nearest neighbours is relaxed, further reductions on the number of distance computations can be obtained. In this work we propose a modification of the LAESA (Linear Approximating and Eliminating Search Algorithm, a fast NN search algorithm for metric spaces) in order to use a certain neighbourhood for lowering error rates and reduce the number of distance computations at the same time.
The authors wish to thank the Spanish CICyT for partial support of this work through project TIC97-0941.
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© 2000 Springer-Verlag Berlin Heidelberg
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Moreno-Seco, F., Micó, L., Oncina, J. (2000). A Fast Approximately k—Nearest—Neighbour Search Algorithm For Clasification Tasks. In: Ferri, F.J., Iñesta, J.M., Amin, A., Pudil, P. (eds) Advances in Pattern Recognition. SSPR /SPR 2000. Lecture Notes in Computer Science, vol 1876. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-44522-6_85
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DOI: https://doi.org/10.1007/3-540-44522-6_85
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