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

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...

is an assignment of distances between the cities for which the nearest neighbor heuristic produces the unique worst possible tour. (If the algorithm is...

In the theory of cluster analysis, the nearest-neighbor chain algorithm is an algorithm that can speed up several methods for agglomerative hierarchical...

input, neighbor joining is guaranteed to find the tree whose distances between taxa agree with it. Neighbor joining may be viewed as a greedy heuristic for...

system) or "find the nearest gas station" (although not taking roads into account). The R-tree can also accelerate nearest neighbor search for various distance...

The unsupervised k-means algorithm has a loose relationship to the k-nearest neighbor classifier, a popular supervised machine learning technique for classification...

recent debate. Like in GLMs, regularization is typically applied. In k-nearest neighbor models, a high value of k leads to high bias and low variance (see...

assignment problem (QAP) Maximum satisfiability problem (MAX-SAT) Nearest neighbor search (by Keinosuke Fukunaga) Flow shop scheduling Cutting stock problem...

HeuristicLab is a software environment for heuristic and evolutionary algorithms, developed by members of the Heuristic and Evolutionary Algorithm Laboratory...

learning models. [1]* Gaussian mixture distance for performing accurate nearest neighbor search for information retrieval. Under an established Gaussian finite...

logarithm. While this is the statement explicitly conjectured by Cramér, his heuristic actually supports the stronger statement lim sup n → ∞ p n + 1 − p n (...

In graph theory, the Weisfeiler Leman graph isomorphism test is a heuristic test for the existence of an isomorphism between two graphs G and H. It is...

algorithms have been developed based on neural networks, decision trees, k-nearest neighbors, naive Bayes, support vector machines and extreme learning machines...

Traveling salesman problem Christofides algorithm Nearest neighbour algorithm Warnsdorff's rule: a heuristic method for solving the Knight's tour problem A*:...

In particular, Mount has worked on the k-means clustering problem, nearest neighbor search, and point location problem. Mount has worked on developing...

where h* is the true cost of the shortest path from n to the nearest goal (the "perfect heuristic"). IDA* is beneficial when the problem is memory constrained...

change from the previous iterations. Clustal aligns sequences using a heuristic that progressively builds a multiple sequence alignment from a set of...

statistically consistent alternatives such as ME. Neighbor joining may be viewed as a greedy heuristic for the balanced minimum evolution (BME) criterion...

predicted using a nearest neighbor model. This model considers only the interactions between a nucleotide and its nearest neighbors on the nucleic acid...

when there is a high number of irrelevant dimensions, and that shared-nearest-neighbor approaches can improve results. Approaches towards clustering in axis-parallel...

given routing protocol, multi-protocol routers must use some external heuristic to select between routes learned from different routing protocols. Cisco...

phase transition behavior, along with non-vanishing long-range and nearest-neighbor spin-spin correlations, deemed relevant to large neural networks as...

traversal to define the farthest-insertion heuristic for the travelling salesman problem. This heuristic finds approximate solutions to the travelling...

solution, which can be generated randomly or according to some sort of nearest neighbor algorithm. To create new solutions, the order that two cities are visited...

PMID 11484054. S2CID 4425080. Ojovan, M.I.; Loshchinin, M.B. (2015). "Heuristic Paradoxes of S.P. Kapitza Theoretical Demography". European Researcher...

avoid local minima. It uses a growing circle around the robot. The nearest neighbors are analyzed first and then the radius of the circle is extended to...

naive string search NAND n-ary function NC NC many-one reducibility nearest neighbor search negation network flow (see flow network) network flow problem...

methods are to connect each data point to its k {\displaystyle k} nearest neighbors or to examples within some distance ϵ {\displaystyle \epsilon } ....

optimal least-squares tree with any correction factor is NP-complete, so heuristic search methods like those used in maximum-parsimony analysis are applied...