Late move reduction - Late move reduction is a selective game-search technique that initially examines some later-ranked moves to a shallower depth than the main search.
Latency - Latency is the elapsed time between a defined initiating event and a defined response or completion event.
Latent class model - A latent class model represents a population as a mixture of unobserved categories, using patterns in observed variables to estimate those categories and...
Latent coincidence analysis - Latent coincidence analysis (LCA) is a supervised latent-variable method for learning a linear transformation in which distances reflect labeled similarity...
Latent diffusion - Latent diffusion is a generative modeling approach that performs a diffusion process in a learned latent representation instead of directly in the original...
Latent semantic analysis - Latent semantic analysis, or LSA, represents relationships among terms and documents by reducing a term - document matrix to a smaller set of numerical...
Latent semantic mapping - Latent semantic mapping, or LSM, is a framework for representing discrete items as vectors in a smaller continuous space, using patterns across a collection...
Latent variable - A latent variable is a variable included in a model but not directly observed in the available data.
Latent variable model - A latent variable model describes observed data using additional variables that are not directly observed in the dataset.
Lateral sound - A lateral sound is a speech sound produced with airflow passing around one or both sides of the tongue rather than through an open central channel.
Latin hypercube design - A Latin hypercube design is a sampling arrangement that spreads a chosen number of runs across every input variable's range or probability distribution.
Latin hypercube sampling - Latin hypercube sampling is a method for selecting combinations of input values so that each input's range is represented evenly through equal-probability...
Latin square - A Latin square is an n-by-n array filled with n distinct symbols so that each symbol appears exactly once in every row and exactly once in every column.
Latin square design - A Latin square design is an experimental arrangement that compares n treatments while blocking two nuisance factors, each having n levels.
Law of large numbers - The law of large numbers describes how an average of many observations approaches an expected value under specified assumptions.
Layer - A layer is a computational component of a neural network that transforms one or more inputs into outputs.
Layer norm - Layer norm, short for layer normalization, is a neural-network operation that normalizes activation values across specified feature dimensions within an...
Layered coding - Layered coding represents information through a base layer and one or more additional layers that provide further detail or capability when decoded with the...
Layered queueing network - A layered queueing network is a performance model that represents software services and the resources they use across interacting layers.
Lazy learning - Lazy learning is a learning approach that postpones substantial generalization until a prediction or query is requested.
LDA-HMM - LDA-HMM is a composite text model combining latent Dirichlet allocation's document-level topics with a hidden Markov model's local sequence structure.
LDL, lower detection limit - LDL, or lower detection limit, describes a measurement method's ability to distinguish a low-level analyte or signal from background under stated conditions.
Leabra - Leabra is a computational neural-modeling framework that combines error-driven and associative learning with biologically motivated activation and...
Leaf - A leaf is an end node of a decision tree: the terminal box where a case finally lands, carrying the tree's prediction for every case that arrives there.
Leak node - A leak node is an additional, usually implicit, cause in a probabilistic model that accounts for an effect occurring through causes not represented...
Lean integration - Lean integration applies lean improvement principles to the continuing work of connecting data, applications and business processes.
Leapfrog integrator - The leapfrog integrator is a numerical method that advances position and momentum through alternating half and full steps.
Learnable evolution model - The learnable evolution model, or LEM, is an evolutionary optimization approach that uses machine learning to generate promising new candidate solutions.
Learnable function class - A learnable function class is a set of candidate functions for which a learning procedure can achieve a specified generalization guarantee from data under...
Learned operator - In computational photography, a learned operator is a function whose behavior is fitted from data to perform an image-processing or prediction task.
Learned sparse retrieval - Learned sparse retrieval is a family of search methods that train models to produce sparse representations for matching queries and documents.
Learning - Learning, in AI, is the process through which a system uses experience or data to develop or improve its ability to perform a task.
Learning automaton - A learning automaton is an adaptive decision mechanism that selects actions, receives feedback from an environment and updates how it chooses future actions.
Learning curve - A learning curve shows how a learning system's measured performance changes as a relevant resource or amount of experience increases.
Learning element - A learning element is the part of a learning-agent architecture that changes the agent's decision-making components using experience and feedback.
Learning rate - Learning rate is the step-size dial of iterative training: how large an adjustment the model makes to its parameters at each update, trading speed of...
Learning rule - A learning rule is a procedure that specifies how a model changes its adjustable parameters or internal state in response to data or feedback.
Learning task - A learning task is the problem a machine-learning system is asked to learn from data.
Learning to rank - Learning to rank is the use of machine learning to order items according to relevance, preference or another defined criterion.
Learning vector quantization - Learning vector quantization, or LVQ, is a family of supervised classification methods that represents classes with labeled prototype vectors.
Least absolute deviation regression - Least absolute deviation regression, often called least absolute deviations or LAD regression, estimates a model by minimizing the sum of absolute residuals...
Least Angle Regression - Least Angle Regression, usually abbreviated LARS, is an algorithm that builds a piecewise-linear path of linear regression fits by progressively...
Least commitment - Least commitment is a search and planning principle that delays choices until they are needed, while making the commitments required for progress.
Least-constraining-value - Least-constraining-value is a value-ordering heuristic for constraint satisfaction problems.
Least privilege - Least privilege is the security principle of giving a person, account, or software component only the access and capabilities needed for its function.
Least squares - Least squares is a fitting principle that chooses model parameters to minimize the sum of squared residuals.
Least-squares Analysis - Least-squares analysis estimates model parameters by minimizing the sum of squared residuals: the differences between observed values and the model's fitted...