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The K-nearest neighbor classification performance can often be significantly improved through metric learning. Popular algorithms are neighbourhood components analysis and large margin nearest neighbor. Supervised metric learning algorithms use the label information to learn a new metric or pseudo-metric.
Nonparametric Discrimination: Consistency Properties," which defined the nearest neighbor rule, an important method that would go on to become a key piece of machine learning technologies, the k-Nearest Neighbor (k-NN) algorithm. [3] She was a Fellow of the Institute of Mathematical Statistics. [4]
This is a list of statistical procedures which can be used for the analysis of categorical data, also known as data on the nominal scale and as categorical variables. General tests [ edit ]
Neighbourhood components analysis is a supervised learning method for classifying multivariate data into distinct classes according to a given distance metric over the data. Functionally, it serves the same purposes as the K-nearest neighbors algorithm and makes direct use of a related concept termed stochastic nearest neighbours .
k-nearest neighbor search identifies the top k nearest neighbors to the query. This technique is commonly used in predictive analytics to estimate or classify a point based on the consensus of its neighbors. k-nearest neighbor graphs are graphs in which every point is connected to its k nearest neighbors.
In statistical analysis, the nearest-neighbor chain algorithm based on following paths in this graph can be used to find hierarchical clusterings quickly. Nearest neighbor graphs are also a subject of computational geometry. The method can be used to induce a graph on nodes with unknown connectivity.
In probability and statistics, a nearest neighbor function, nearest neighbor distance distribution, [1] nearest-neighbor distribution function [2] or nearest neighbor distribution [3] is a mathematical function that is defined in relation to mathematical objects known as point processes, which are often used as mathematical models of physical phenomena representable as randomly positioned ...
Structured k-nearest neighbours (SkNN) [1] [2] [3] is a machine learning algorithm that generalizes k-nearest neighbors (k-NN). k-NN supports binary classification, multiclass classification, and regression, [4] whereas SkNN allows training of a classifier for general structured output.