Nearest-neighbor Density Estimator
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Nearest-neighbor Density Estimator
Nearest neighbor may refer to: * Nearest neighbor search in pattern recognition and in computational geometry * Nearest-neighbor interpolation for interpolating data * Nearest neighbor graph in geometry * Nearest neighbor function in probability theory * Nearest neighbor decoding in coding theory * The k-nearest neighbor algorithm in machine learning, an application of generalized forms of nearest neighbor search and interpolation * The nearest neighbour algorithm for approximately solving the travelling salesman problem * The nearest-neighbor thermodynamic parameters for determining the thermodynamics of nucleic acids * The nearest neighbor clustering for calculating distances between clusters in hierarchical clustering See also * Moore neighborhood * Von Neumann neighborhood In cellular automata, the von Neumann neighborhood (or 4-neighborhood) is classically defined on a two-dimensional square lattice and is composed of a central cell and its four adjacent cells. Th ...
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Nearest Neighbor Search
Nearest neighbor search (NNS), as a form of proximity search, is the optimization problem of finding the point in a given set that is closest (or most similar) to a given point. Closeness is typically expressed in terms of a dissimilarity function: the less similar the objects, the larger the function values. Formally, the nearest-neighbor (NN) search problem is defined as follows: given a set ''S'' of points in a space ''M'' and a query point ''q'' ∈ ''M'', find the closest point in ''S'' to ''q''. Donald Knuth in vol. 3 of '' The Art of Computer Programming'' (1973) called it the post-office problem, referring to an application of assigning to a residence the nearest post office. A direct generalization of this problem is a ''k''-NN search, where we need to find the ''k'' closest points. Most commonly ''M'' is a metric space and dissimilarity is expressed as a distance metric, which is symmetric and satisfies the triangle inequality. Even more common, ''M'' is take ...
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