Divide-and-conquer algorithm - A divide-and-conquer algorithm solves a problem by splitting it into smaller instances, solving those instances, and combining their answers.
Division model (Fitzgibbon) - The division model associated with Fitzgibbon is a compact mathematical model of radial lens distortion.
Divisive clustering - Divisive clustering is a top-down approach to grouping data: start with everything in one cluster and repeatedly split the least coherent group until each...
Divisive Communities - Divisive communities are groups identified by progressively splitting a graph into smaller parts.
Dixon's Q test - Dixon's Q test is an outlier test based on ratios of gaps between ordered observations.
DMAIC - DMAIC stands for Define, Measure, Analyze, Improve and Control.
DOACROSS parallelism - DOACROSS parallelism executes work from different loop iterations concurrently while preserving required dependencies between those iterations.
Document-at-a-time - Document-at-a-time is a search-query evaluation strategy that combines the contributions of query terms for one document before moving to another document.
Document classification - Document classification assigns one or more category labels to a document based on its content or other relevant information.
Document clustering - Document clustering is the process of grouping documents according to patterns of similarity in their representations.
Document-oriented database - A document-oriented database stores and queries records as structured documents, commonly containing named fields, nested objects and arrays.
Document partitioning - Document partitioning divides a search collection into subsets of documents and maintains a local index for each subset.
Document retrieval - Document retrieval is the process of finding documents that are relevant to a user's information need within a collection.
Document-rotation denoising - Document-rotation denoising is a language-model pretraining task in which a document is cyclically shifted to start at a randomly chosen token, and the...
Document structuring - Document structuring is the natural-language-generation task of organizing selected information into a coherent document.
Document-term matrix - A document-term matrix is a numerical table representing documents through selected text features.
DOE mean plot - A DOE mean plot shows the average response at each level of factors in a designed experiment. DOE stands for design of experiments.
Domain - A domain is the area, setting, or set of inputs to which a model, task, or body of knowledge applies. The precise meaning depends on context.
Domain adaptation - Domain adaptation adjusts a machine-learning system so that knowledge learned from one data setting can be useful in another.
Domain closure - Domain closure is an assumption that every object in a modeled domain is accounted for by the specified names or ground terms.
Domain Constraint - A domain constraint restricts which values are valid for an attribute or type.
Domain driven data mining - Domain driven data mining is an approach to discovering useful patterns that incorporates the knowledge, objectives, constraints, and working practices of...
Domain elements - Domain elements are the objects that belong to the domain of a logical model. They are what variables range over and what names or other terms can refer to.
Domain generalization - Domain generalization is the task of learning from available environments so that a model works well in a new environment whose data were not used to train...
Domain shift - Domain shift is a change in the data conditions between the setting where an AI model was developed or evaluated and another setting where it is used.
Domain (spatial) operation - In the supplied computational-photography glossary, a domain (spatial) operation changes where image information is sampled: an output pixel obtains its...
Dominance-based rough set approach - The dominance-based rough set approach, or DRSA, analyses decisions involving ordered criteria and ordered outcome classes.
Dominant resource fairness - Dominant resource fairness, or DRF, is a resource-allocation rule that extends max-min fairness to several resource types.
Dominant strategy - A dominant strategy is a strategy that gives a player an outcome at least as good as every alternative, whatever strategies the other players choose, under...
Dominant strategy equilibrium - A dominant strategy equilibrium is a joint choice of strategies in which every player uses a dominant strategy.
Dominating policy - A dominating policy, in the Markov decision-process sense used here, is a policy whose expected return is at least as high as that of every alternative...
Donor (imputation) - A donor in imputation is a record that supplies values used to replace missing or inconsistent values in another record, called the recipient.
DOPIPE - DOPIPE is a form of loop-level parallelism that divides work from a loop into cooperating pipeline stages.
Dormand - Prince method - The Dormand - Prince method is an explicit Runge - Kutta method for numerically solving ordinary differential equations with initial values.
Dot plot - A dot plot, in the distribution-display sense used by the source glossary, represents observations as dots positioned along a measurement axis, with...
Dot product - The dot product of two real-valued vectors is the sum of the products of their corresponding components.
Dot-product attention - Dot-product attention is a mechanism that scores the compatibility of a query vector with key vectors using their dot products, converts those scores into...
Double deep Q-network - A double deep Q-network, usually called Double DQN, is a reinforcement-learning method that separates choosing a promising next action from estimating that...
Double delta feature - A double delta feature is the change of a change: it measures how quickly a measurement is speeding up or slowing down, rather than how fast it is simply...
Double hashing - Double hashing is an open-addressing method for resolving collisions in a hash table.
Double machine learning - Double machine learning (DML), also called double/debiased machine learning, is a framework for estimating a target statistical or causal parameter while...
Double mass analysis - Double mass analysis compares the cumulative totals of two related data series to examine whether their relationship remains consistent over time.
Double moving average - A double moving average is a moving average applied to a series that has already been smoothed by a moving average.
Double Q-learning - Double Q-learning is an off-policy reinforcement-learning method that uses two action-value estimates to separate choosing a promising next action from...
Double sampling - Double sampling, in the OECD glossary's principal industrial-inspection sense, is an acceptance-sampling plan that permits a decision after a first sample...
Double-sampling problem in Bellman-error minimization - The double-sampling problem in Bellman-error minimization is the difficulty of obtaining an unbiased gradient estimate when that gradient contains a product...
Double-spending - Double-spending is an attempt or failure in which the same digital asset or spendable value is used more than once in conflicting transactions.
Doubly block circulant - Doubly block circulant describes a matrix built from a circulant arrangement of blocks, with each block itself circulant.