In statistics, the k-nearest neighbors algorithm (k-NN) is a non-parametric supervised learning method first developed by Evelyn Fix and Joseph Hodges...
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Dimension reduction Fixed-radius near neighbors Fourier analysis Instance-based learning k-nearest neighbor algorithm Linear least squares Locality sensitive...
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Nearest neighbor graph in geometry Nearest neighbor function in probability theory Nearest neighbor decoding in coding theory The k-nearest neighbor algorithm...
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have different shapes. The unsupervised k-means algorithm has a loose relationship to the k-nearest neighbor classifier, a popular supervised machine...
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In the theory of cluster analysis, the nearest-neighbor chain algorithm is an algorithm that can speed up several methods for agglomerative hierarchical...
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margin nearest neighbor (LMNN) classification is a statistical machine learning algorithm for metric learning. It learns a pseudometric designed for k-nearest...
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theoretical discussions of algorithms a kind of general position is often assumed, namely, the nearest (k-nearest) neighbor is unique for each object....
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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...
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the same purposes as the K-nearest neighbors algorithm and makes direct use of a related concept termed stochastic nearest neighbours. Neighbourhood...
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Dimensionality reduction (redirect from Dimensionality reduction algorithm)
dimension reduction is usually performed prior to applying a K-nearest neighbors algorithm (k-NN) in order to avoid the effects of the curse of dimensionality...
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The primary motivation for employing lazy learning, as in the K-nearest neighbors algorithm, used by online recommendation systems ("people who viewed/purchased/listened...
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Outline of machine learning (redirect from Machine learning algorithms)
Hierarchical clustering k-means clustering k-medians Mean-shift OPTICS algorithm Anomaly detection k-nearest neighbors algorithm (k-NN) Local outlier factor...
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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...
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instance away. Examples of instance-based learning algorithms are the k-nearest neighbors algorithm, kernel machines and RBF networks.:βch. 8β These store...
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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...
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non-parametric models for regression. nearest neighbors, see nearest-neighbor interpolation and k-nearest neighbors algorithm regression trees kernel regression...
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Transduction (machine learning) (redirect from Transduction algorithm)
learning algorithm is the k-nearest neighbor algorithm, which is related to transductive learning algorithms. Another example of an algorithm in this category...
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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...
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reconstructed from K nearest neighbors, as measured by Euclidean distance. For such an implementation the algorithm has only one free parameter K, which can be...
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Supervised learning (redirect from Algorithms for supervised learning)
regression Naive Bayes Linear discriminant analysis Decision trees K-nearest neighbor algorithm Neural networks (Multilayer perceptron) Similarity learning Given...
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scaling and rotation algorithm for sprites developed by Xenowhirl. It produces far fewer artifacts than nearest-neighbor rotation algorithms, and like EPX,...
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Single-linkage clustering (redirect from Nearest neighbor clustering)
matter; in this application, it is also known as the friends-of-friends algorithm. In the beginning of the agglomerative clustering process, each element...
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Ball tree (section k-d construction algorithm)
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...
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{\mu }}_{\ell }-{\vec {x}}\|} . Cluster hypothesis k-means clustering k-nearest neighbor algorithm Linear discriminant analysis Manning, Christopher;...
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Hierarchical navigable small world (category Search algorithms)
small world (HNSW) algorithm is a graph-based approximate nearest neighbor search technique used in many vector databases. Nearest neighbor search without...
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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...
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Nucleic acid thermodynamics (redirect from Nearest neighbor thermodynamic algorithm)
calculated to be β22.4 kJ/mol. The experimental value is β21.8 kJ/mol. The parameters associated with the ten groups of neighbors shown in table 1 are determined...
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a class of unsupervised learning algorithms for grouping and bucketing related input vector. k-nearest neighbors (k-NN): a method for classifying objects...
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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)...
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dictate which approach is most suitable in any particular case. k-nearest neighbors algorithm Logistic regression Support Vector Machines Decision Tree Learning...
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