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

Dimension reduction Fixed-radius near neighbors Fourier analysis Instance-based learning k-nearest neighbor algorithm Linear least squares Locality sensitive...

Nearest neighbor graph in geometry Nearest neighbor function in probability theory Nearest neighbor decoding in coding theory The k-nearest neighbor algorithm...

have different shapes. The unsupervised k-means algorithm has a loose relationship to the k-nearest neighbor classifier, a popular supervised machine...

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

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

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

nearest neighbors of the query point is significantly less than the average distance between the query point and each of the k nearest neighbors, the performance...

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

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

The primary motivation for employing lazy learning, as in the K-nearest neighbors algorithm, used by online recommendation systems ("people who viewed/purchased/listened...

Hierarchical clustering k-means clustering k-medians Mean-shift OPTICS algorithm Anomaly detection k-nearest neighbors algorithm (k-NN) Local outlier factor...

k-nearest neighbors algorithm (k-NN), a method for classifying objects Nearest neighbor graph (k-NNG), a graph connecting each point to its k nearest...

instance away. Examples of instance-based learning algorithms are the k-nearest neighbors algorithm, kernel machines and RBF networks.: ch. 8  These store...

learning algorithm is the k-nearest neighbor algorithm, which is related to transductive learning algorithms. Another example of an algorithm in this category...

in its immediate neighborhood. This is the bias used in the k-nearest neighbors algorithm. The assumption is that cases that are near each other tend...

that is specifically designed to facilitate the speed-up of a k-nearest neighbors algorithm in finite metric spaces. Compressed cover tree is a simplified...

hyperparameter in the algorithm is what counts as a "neighbor" of a point. Generally the data points are reconstructed from K nearest neighbors, as measured by...

regression Naive Bayes Linear discriminant analysis Decision trees K-nearest neighbor algorithm Neural networks (Multilayer perceptron) Similarity learning Given...

calculated to be −22.4 kJ/mol. The experimental value is −21.8 kJ/mol. The parameters associated with the ten groups of neighbors shown in table 1 are determined...

dictate which approach is most suitable in any particular case. k-nearest neighbors algorithm Logistic regression Support Vector Machines Decision Tree Learning...

{\mu }}_{\ell }-{\vec {x}}\|} . Cluster hypothesis k-means clustering k-nearest neighbor algorithm Linear discriminant analysis Manning, Christopher;...

scaling and rotation algorithm for sprites developed by Xenowhirl. It produces far fewer artifacts than nearest-neighbor rotation algorithms, and like EPX,...

(those whose nearest neighbors are too far away). DBSCAN is one of the most commonly used and cited clustering algorithms. In 2014, the algorithm was awarded...

integral over a region of space, and the nearest centroid operation results in Voronoi diagrams. Although the algorithm may be applied most directly to the...

against which the covariates are balanced out (similar to the K-nearest neighbors algorithm). By matching treated units to similar non-treated units, matching...

matter; in this application, it is also known as the friends-of-friends algorithm. In the beginning of the agglomerative clustering process, each element...

non-parametric models for regression. nearest neighbors, see nearest-neighbor interpolation and k-nearest neighbors algorithm regression trees kernel regression...

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)...

The ball tree nearest-neighbor algorithm examines nodes in depth-first order, starting at the root. During the search, the algorithm maintains a max-first...