- nearest neighbor classification
- Механика: классификация по методу ближайшего соседа
Универсальный англо-русский словарь. Академик.ру. 2011.
nearest neighbor classification
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Nearest neighbor search — (NNS), also known as proximity search, similarity search or closest point search, is an optimization problem for finding closest points in metric spaces. The problem is: given a set S of points in a metric space M and a query point… … Wikipedia
Nearest-neighbor chain algorithm — In the theory of cluster analysis, the nearest neighbor chain algorithm is a method that can be used to perform several types of agglomerative hierarchical clustering, using an amount of memory that is linear in the number of points to be… … Wikipedia
k-nearest neighbor algorithm — KNN redirects here. For other uses, see KNN (disambiguation). In pattern recognition, the k nearest neighbor algorithm (k NN) is a method for classifying objects based on closest training examples in the feature space. k NN is a type of instance… … Wikipedia
K-nearest neighbor algorithm — In pattern recognition, the k nearest neighbor algorithm ( k NN) is a method for classifying objects based on closest training examples in the feature space. k NN is a type of instance based learning, or lazy learning where the function is only… … Wikipedia
Statistical classification — See also: Pattern recognition See also: Classification test In machine learning, statistical classification is the problem of identifying the sub population to which new observations belong, where the identity of the sub population is unknown, on … Wikipedia
Curse of dimensionality — The curse of dimensionality refers to various phenomena that arise when analyzing and organizing high dimensional spaces (often with hundreds or thousands of dimensions) that do not occur in low dimensional settings such as the physical space… … Wikipedia
Cluster analysis — The result of a cluster analysis shown as the coloring of the squares into three clusters. Cluster analysis or clustering is the task of assigning a set of objects into groups (called clusters) so that the objects in the same cluster are more… … Wikipedia
Caltech 101 — is a dataset of digital images created in September, 2003, compiled by Fei Fei Li, Marco Andreetto, and Marc Aurelio Ranzato at the California Institute of Technology. It is intended to facilitate Computer Vision research and techniques. It is… … Wikipedia
Features (pattern recognition) — In pattern recognition, features are the individual measurable heuristic properties of the phenomena being observed. Choosing discriminating and independent features is key to any pattern recognition algorithm being successful in classification.… … Wikipedia
Cluster assumption — The cluster assumption is a type of data modeling used in machine learning specifically in Supervised learning and Semi supervised learning. It states that if points are in the same cluster, they are likely to be of the same class.[1] There may… … Wikipedia
Scale-invariant feature transform — Feature detection Output of a typical corner detection algorithm … Wikipedia
