Indicator function - An indicator function represents whether a condition holds by returning one when it does and zero when it does not.
Infimum (or greatest lower bound) - The infimum of a nonempty set of real numbers bounded below is its greatest lower bound: the largest number that is no greater than every member of the set.
Information projection - Information projection, often called I-projection, finds the probability distribution in an allowed set that minimizes a specified Kullback-Leibler...
Inner product - An inner product is a rule that combines two vectors into a scalar while satisfying specific symmetry, linearity, and positivity properties.
Interior-point method - An interior-point method is an optimization approach that solves constrained problems using a sequence of points associated with the interior of the...
Interval algebra - Interval algebra, in temporal reasoning, is a formal system for describing and combining relationships between time intervals.
Inverse matrix - An inverse matrix reverses the linear transformation represented by an invertible square matrix.
Item matrix - An item matrix is a collection of learned item representations used in a matrix-factorization recommender.
Iterated local search - Iterated local search is an optimization framework that repeatedly improves a solution locally, perturbs it, improves the perturbed solution, and decides...
Iterative method - An iterative method is a numerical procedure that constructs a sequence of approximate answers by repeatedly applying an update rule.
Iterative rational Krylov algorithm - The iterative rational Krylov algorithm, or IRKA, constructs a smaller linear dynamical model by repeatedly updating interpolation points using the poles of...
Jacobi method - The Jacobi method is an iterative algorithm for solving a linear system Ax=b.
Jacobian - The Jacobian is the matrix of first-order partial derivatives of a vector-valued function.
Jenkins - Traub algorithm - The Jenkins-Traub algorithm is a family of numerical methods for finding polynomial zeros through three stages of auxiliary-polynomial iteration.
Joint source and channel coding - Joint source and channel coding designs information representation and protection against transmission noise together.
Kahan summation algorithm - The Kahan summation algorithm is a compensated method for adding floating-point numbers.
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.
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.
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.
Kronecker product - The Kronecker product combines two matrices into a larger block matrix by replacing every entry of the first matrix with that entry multiplied by the entire...
Kullback-Leibler divergence - Kullback-Leibler divergence measures how one probability distribution differs from another in a particular direction.
Label space - A label space is the set of all permitted target values for a machine-learning task. Usually written Y, it describes what the system is intended to predict.
Label vector - A label vector is an ordered collection of target values aligned with the examples in a dataset.
Labeled data point - A labeled data point is an example for which a target value has been supplied or determined.
Laplacian matrix - A Laplacian matrix is a matrix that represents differences across connections in a graph.
Law of large numbers - The law of large numbers describes how an average of many observations approaches an expected value under specified assumptions.
Learning task - A learning task is the problem a machine-learning system is asked to learn from data.
Least squares - Least squares is a fitting principle that chooses model parameters to minimize the sum of squared residuals.
Least-squares minimization - Least-squares minimization is the process of finding parameters that make a collection of residuals as small as possible under a squared-error objective.
Lebesgue integral - The Lebesgue integral is a way to add up a measurable function's values with respect to a measure. The measure specifies how much weight different sets receive.
Legendre pseudospectral method - A Legendre pseudospectral method approximates continuous functions with polynomials associated with Legendre-based nodes and enforces governing equations at...
Lemke's algorithm - Lemke's algorithm is a complementary-pivot method for solving a linear complementarity problem.
Level set (data structures) - A level-set data structure stores sampled values of a function whose chosen contour, usually the zero contour, represents a curve or surface.
Level-set method - The level-set method represents a moving curve or surface implicitly as a level of a function, usually its zero level, and evolves that function using a...
Levenberg - Marquardt algorithm - The Levenberg-Marquardt algorithm is an iterative method for nonlinear least-squares problems.
Levenshtein Distance - Levenshtein distance is the minimum number of single-symbol insertions, deletions, and substitutions needed to transform one sequence into another, when...
Lexicographic max-min optimization - Lexicographic max-min optimization chooses a feasible outcome by first maximizing its smallest benefit, then its second-smallest benefit without worsening...
Lexicographic optimization - Lexicographic optimization handles several objectives in a fixed priority order.
Linear classifier - A linear classifier assigns categories using linear or affine scores of the input features.
Linear discriminant analysis - Linear discriminant analysis, or LDA, is a supervised method used for classification and for finding linear projections that separate known classes.
Linear least squares - Linear least squares chooses coefficients for a model that is linear in those coefficients by minimizing a sum of squared residuals.
Linear map - A linear map is a function between vector spaces that preserves addition and scalar multiplication.
Linear network coding - Linear network coding allows an intermediate network node to transmit linear combinations of received data instead of only forwarding or copying individual...
Linear programming - Linear programming is optimization of a linear objective subject to linear equality and inequality constraints.
Linear search problem - The linear search problem studies how to find a target at an unknown location on a line when a searcher starts at a known point and does not know which...
Lipschitz continuity - Lipschitz continuity is a bound on how quickly a function's output can change relative to its input.