k-anonymity - k-anonymity is a property of a released table in which each record shares its combination of designated quasi-identifier values with at least k − 1 other...
K-components - K-components are maximal parts of an undirected graph whose node connectivity is at least k.
k-d tree - A k-d tree is a binary spatial index that recursively divides points using coordinate-aligned boundaries.
K-edge-components - K-edge-components are maximal groups of vertices for which every pair has at least k edge-disjoint connecting paths in the original graph.
k-fold cross validation - K-fold cross validation is an evaluation procedure that divides data into k non-overlapping parts, or folds, and repeatedly trains a model on all but one...
K-independent hashing - K-independent hashing uses a randomly chosen hash function from a family whose outputs on any k distinct fixed keys are independent and uniformly distributed.
K-line (artificial intelligence) - A K-line is a proposed memory mechanism in Marvin Minsky's theory of mind: a connection structure that can reactivate selected mental processes associated...
k-means - K-means is a clustering method that groups numerical observations around k centers, minimizing the sum of squared Euclidean distances between observations...
k-median - K-median is a clustering approach that seeks k representative centers while minimizing the total distance from observations to their assigned centers.
K-nearest Neighbor Classification Algorithm - K-nearest Neighbor Classification is a method that classifies a case by finding the k most similar labeled cases in the training data and letting them vote...
k-nearest neighbors - K-nearest neighbors, often written k-NN, is a method that uses the k closest reference examples to make a prediction or retrieve similar cases.
k-nearest neighbors regression - K-nearest neighbors regression predicts a numerical target by combining the target values of the k training observations closest to a new input.
K-node-cutsets - K-node-cutsets are sets of k vertices whose removal separates a graph.
K-optimal pattern discovery - K-optimal pattern discovery is an exploratory data-mining approach that seeks the k best patterns according to a specified measure and set of constraints.
Kahan summation algorithm - The Kahan summation algorithm is a compensated method for adding floating-point numbers.
Kaiser - Meyer - Olkin test - The Kaiser - Meyer - Olkin measure, commonly called the KMO test, is a diagnostic index used when assessing whether a correlation matrix is suitable for...
Kalman filter - A Kalman filter is a recursive method for estimating a hidden state in a linear dynamic system from noisy observations.
Kalman gain matrix - The Kalman gain matrix determines how a Kalman filter converts a new measurement's prediction error into a correction of its estimated state.
Kaniadakis distribution - A Kaniadakis distribution is a probability model constructed within the κ-deformed statistical framework associated with Giorgio Kaniadakis.
Kaniadakis exponential distribution - A Kaniadakis exponential distribution is a κ-deformed alternative to the ordinary exponential distribution. The name is used for more than one construction.
Kaniadakis Gamma distribution - The Kaniadakis Gamma distribution is a κ-deformed family for positive random variables, built by replacing the exponential component of a generalized Gamma...
Kaniadakis Gaussian distribution - The Kaniadakis Gaussian distribution, or κ-Gaussian, is a symmetric probability family obtained by normalizing a κ-exponential of a negative quadratic...
Kaniadakis Weibull distribution - The Kaniadakis Weibull distribution, or κ-Weibull, is a positive-valued probability model commonly defined by the survival function S(x) = expκ(−βx^α), with...
Kaplan-Meier estimator - The Kaplan - Meier estimator is a nonparametric estimate of a survival function from time-to-event observations, including right-censored observations.
Karmarkar's algorithm - Karmarkar's algorithm is a polynomial-time method for linear programming introduced by Narendra Karmarkar in 1984.
Karush-Kuhn-Tucker conditions - The Karush-Kuhn-Tucker conditions, or KKT conditions, are equations and inequalities that characterize certain solutions of constrained optimization problems.
Keepout - A keepout is a designated area that autonomous robot navigation should avoid or must not enter.
Kendall's tau - Kendall's tau is a rank-based measure of association between two variables, calculated by comparing the ordering of pairs of observations.
Kernel - A kernel is a function that measures the similarity of two cases, used by kernel methods to work in an enriched feature space without ever computing the...
Kernel density estimation - Kernel density estimation is a nonparametric method for estimating a probability density from observed data.
Kernel embedding of distributions - Kernel embedding of distributions represents a probability distribution as an average of feature representations in a reproducing kernel Hilbert space.
Kernel function - In kernel-based machine learning, a kernel function computes an inner product between feature representations of two inputs.
Kernel (kernel method) - A kernel, in kernel-based machine learning, is a function that evaluates an inner product between representations of two inputs in a feature space.
Kernel method - A kernel method is a learning or approximation method that uses a kernel to operate through inner products in a feature space.
Kernel methods for vector output - Kernel methods for vector output extend kernel learning to predict several output values together.
Kernel perceptron - A kernel perceptron is a perceptron classifier expressed through kernel evaluations between examples.
Kernel regression - Kernel regression estimates a relationship between predictors and an outcome using locally weighted observations.
Kernel ridge regression - Kernel ridge regression combines squared-error regression with a penalty on function complexity in a kernel-defined feature space.
Kernel Support Vector Machines - Kernel Support Vector Machines are SVMs equipped with a kernel function, letting them find non-linear boundaries by working implicitly in an enriched...
Kernel trick - The kernel trick rewrites a compatible learning algorithm so that feature-space inner products are computed through a kernel function instead of by...
Key Constraint - A key constraint requires a specified attribute, or combination of attributes, to identify rows uniquely within a relation or table.
Key - value database - A key-value database stores information as pairs consisting of a key and its associated value.
Key Value (KV) store - A key-value store is a data-storage system that associates a key with a value and supports retrieving that value using its key.
Keypoints - Keypoints are specific, meaningful locations detected on an object or image, such as the corners of a document, the joints of a body or the landmarks of a...
Keyspace (distributed data store) - A keyspace in a distributed data store is a logical naming scope used to organize data, with the exact meaning depending on the system.
Keystoning - Keystoning is a perspective effect in which an intended rectangular image or planar subject appears trapezoidal, often because the projection or viewing...