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k. -nearest neighbors algorithm. In statistics, the k-nearest neighbors algorithm ( k-NN) is a non-parametric supervised learning method first developed by Evelyn Fix and Joseph Hodges in 1951, [1] and later expanded by Thomas Cover. [2] It is used for classification and regression. In both cases, the input consists of the k closest training ...
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.
Structured k-Nearest Neighbours is a machine learning algorithm that generalizes the k-Nearest Neighbors (kNN) classifier. Whereas the kNN classifier supports binary classification, multiclass classification and regression, the Structured kNN (SkNN) allows training of a classifier for general structured output labels.
In computer science, a k-d tree (short for k-dimensional tree) is a space-partitioning data structure for organizing points in a k -dimensional space. K-dimensional is that which concerns exactly k orthogonal axes or a space of any number of dimensions.
The associative neural network (ASNN) is an extension of committee of machines that combines multiple feedforward neural networks and the k-nearest neighbor technique. It uses the correlation between ensemble responses as a measure of distance amid the analyzed cases for the kNN. This corrects the Bias of the neural network ensemble.
Bias and variance as function of model complexity. In statistics and machine learning, the bias–variance tradeoff describes the relationship between a model's complexity, the accuracy of its predictions, and how well it can make predictions on previously unseen data that were not used to train the model. In general, as we increase the number ...
Lazy learning. In machine learning, lazy learning is a learning method in which generalization of the training data is, in theory, delayed until a query is made to the system, as opposed to eager learning, where the system tries to generalize the training data before receiving queries. [1]
Large margin nearest neighbor ( LMNN) [1] classification is a statistical machine learning algorithm for metric learning. It learns a pseudometric designed for k-nearest neighbor classification. The algorithm is based on semidefinite programming, a sub-class of convex optimization . The goal of supervised learning (more specifically ...