The nearest neighbour algorithm was one of the first algorithms used to solve the travelling salesman problem approximately. In that problem, the salesman...

In statistics, the k-nearest neighbors algorithm (k-NN) is a non-parametric supervised learning method. It was first developed by Evelyn Fix and Joseph...

optical character recognition Statistical classification – see k-nearest neighbor algorithm Computer vision – for point cloud registration Computational geometry...

points around (neighboring) that point. The nearest neighbor algorithm selects the value of the nearest point and does not consider the values of neighboring...

neighbor algorithm in machine learning, an application of generalized forms of nearest neighbor search and interpolation The nearest neighbour algorithm for...

on. Examples include Dijkstra's algorithm, Kruskal's algorithm, the nearest neighbour algorithm, and Prim's algorithm. Another important subclass of this...

In theoretical discussions of algorithms a kind of general position is often assumed, namely, the nearest (k-nearest) neighbor is unique for each object...

the better the algorithm is. These algorithms include: Interpolation algorithm, Nearest neighbour algorithm, Clark & Wright algorithm, Double spanning...

whose elements are involved to be merged. The method is also known as nearest neighbour clustering. The result of the clustering can be visualized as a dendrogram...

salesman problem Christofides algorithm Nearest neighbour algorithm Vehicle routing problem Clarke and Wright Saving algorithm Warnsdorff's rule: a heuristic...

problem, considers the obvious brute-force algorithm, and observes the non-optimality of the nearest neighbour heuristic: We denote by messenger problem...

A. M.; Frank, E. (2007). "An Empirical Comparison of Exact Nearest Neighbour Algorithms". Knowledge Discovery in Databases: PKDD 2007 (PDF). Lecture...

Hamiltonian path problem Knight's tour Traveling salesman problem Nearest neighbour algorithm Bottleneck traveling salesman problem Path analysis (paths and...

which the nearest-neighbour heuristic produces the unique worst possible tour. For other possible examples, see horizon effect. Greedy algorithms can be...

variants are adaptive k-d trees. The nearest neighbour search (NN) algorithm aims to find the point in the tree that is nearest to a given input point. This search...

maximised by minimising d. Minimum distance decoding is also known as nearest neighbour decoding. It can be assisted or automated by using a standard array...

Best bin first is a search algorithm that is designed to efficiently find an approximate solution to the nearest neighbor search problem in very-high-dimensional...

taylor-kehitelmana [The representation of the cumulative rounding error of an algorithm as a Taylor expansion of the local rounding errors] (PDF) (Thesis) (in...

the span of a few minutes. There are four common track algorithms: Nearest neighbour algorithm Probabilistic Data Association Multiple Hypothesis Tracking...

of the (2 π)D/2 term. This produces a generalization of the k-nearest neighbour algorithm. That is, a uniform kernel function will return the KNN technique...

Large margin nearest neighbor (LMNN) classification is a statistical machine learning algorithm for metric learning. It learns a pseudometric designed...

Rachael; McDonnell, Rachael A.; Lloyd, Christopher D. (2015). "8.11 Nearest neighbours: Thiessen (Dirichlet/Voroni) polygons". Principles of Geographical...

Mailund, Thomas; Pedersen, Christian N. S. (2008). "Rapid Neighbour-Joining" (PDF). Algorithms in Bioinformatics. Lecture Notes in Computer Science. Vol...

In time series analysis, dynamic time warping (DTW) is an algorithm for measuring similarity between two temporal sequences, which may vary in speed....

classifier Soft set-based classifier Support vector machines (SVM) K-nearest neighbour algorithms tf–idf Classification techniques have been applied to spam filtering...

every node has a label that the maximum number of their neighbours have, then stop the algorithm. Else, set t = t + 1 and go to (3). Label propagation offers...

into the thermodynamics of DNA hybridization and the validity of the nearest-neighbour model as well. A more realistic way of modeling the behavior of nucleic...

same purposes as the K-nearest neighbors algorithm and makes direct use of a related concept termed stochastic nearest neighbours. Neighbourhood components...

function (V-linkage). The product of in-degree and out-degree on a k-nearest-neighbour graph (graph degree linkage). The increment of some cluster descriptor...

Tremblay, G. (2004). Optimizing Nearest Neighbour in Random Subspaces using a Multi-Objective Genetic Algorithm (PDF). 17th International Conference...