Dominance-based rough set approach - The dominance-based rough set approach, or DRSA, analyses decisions involving ordered criteria and ordered outcome classes.
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...
DPLL algorithm - The DPLL algorithm is a complete search procedure for deciding whether a propositional-logic formula in conjunctive normal form can be satisfied.
Dynamic epistemic logic - Dynamic epistemic logic studies how knowledge and beliefs change when information is communicated or events are observed.
Eager learning - Eager learning builds a general predictive model from training examples before new prediction requests arrive.
Early stopping - Early stopping is ending a model's training at the point where it performs best on fresh data, before it starts memorizing the training set.
Einsum notation - Einsum notation is a compact way of writing operations on multi-dimensional arrays by naming their axes, so sums, products, transposes and contractions are...
Elastic-Net - Elastic-Net is a regression method that combines the two classic penalty types, L1 and L2, so it selects variables like the first and stabilizes estimates...
ELMo - ELMo, short for Embeddings from Language Models, is a method for producing word representations that change with the surrounding text.
Embodied agent - An embodied agent perceives and acts through a body situated in an environment.
Emergent algorithm - An emergent algorithm produces a larger pattern of behavior through interactions among simpler components following local rules.
Empirical dynamic modeling - Empirical dynamic modeling, or EDM, is a family of methods for studying and predicting changing systems by reconstructing their dynamics from observed...
Empowerment (artificial intelligence) - In AI, empowerment is an information-theoretic measure of how much an agent's actions can influence its future observations.
Encoder - An encoder is the part of a model that reads an input and compresses it into an internal numeric form that captures what matters about it for the task.
Encoding (for Features) - Encoding, for features, is converting a field into a numeric form a model can use, such as turning the category red, amber or green into numbers without...
Energy-based model - An energy-based model assigns a numerical energy score to a possible configuration or an input-output combination.
Ensemble - An ensemble is a model made by combining several models, so that their collective answer is better than any of them alone.
Ensemble learning - Ensemble learning is the discipline of training multiple models and combining them deliberately, covering the strategies, bagging, boosting and stacking...
Enterprise cognitive system - An enterprise cognitive system is a broad label for business software that combines AI capabilities with organisational data and workflows to support tasks...
Environment - In AI, an environment is the world an agent acts in: everything outside the agent that it can observe, influence and receive consequences from.
Environment grounding - Environment grounding is anchoring an agent's outputs in the actual state of its environment, through fresh observations, tool results or retrieved data...
Episode - An episode is one complete run of an agent's task, from its starting situation to a natural end, such as one game, one delivery or one customer conversation.
Episodic memory - Episodic memory is an agent's store of specific past experiences it can look back at: particular events, conversations or attempts, rather than generalized...
Epistemic modal logic - Epistemic modal logic is a formal system for reasoning about knowledge, including what one agent knows about the world and about other agents' knowledge.
Epoch - An epoch is one complete pass through the entire training dataset during a model's training; training usually runs for many epochs.
Error-driven learning - Error-driven learning adjusts a model using a discrepancy between its output and a desired or observed outcome.
Estimation of distribution algorithm - An estimation of distribution algorithm, or EDA, is an optimization method that learns a probability model of promising candidate solutions and samples that...
Evaluation of binary classifiers - Evaluation of binary classifiers measures how well a system distinguishes between two outcome classes.
Evolutionary acquisition of neural topologies - Evolutionary acquisition of neural topologies, usually shortened to EANT, is a neuroevolution method that searches for both a neural network's structure and...
Evolutionary computation - Evolutionary computation is a family of computational search methods inspired by variation and selection in biological evolution.
Evolutionary developmental robotics - Evolutionary developmental robotics studies robot design and behavior by combining evolutionary search across generations with development within a robot's...
Evolutionary multi-objective optimization - Evolutionary multi-objective optimization uses evolutionary search to find useful solutions when several objectives must be considered together.
Evolutionary multimodal optimization - Evolutionary multimodal optimization uses evolutionary search to discover and retain multiple distinct optima or promising solution regions within an...
Evolvability (computer science) - In computer science, evolvability can refer specifically to Leslie Valiant's mathematical framework for asking which mechanisms can emerge through...
Evolving classification function - An evolving classification function is a classifier that can adjust its representation as data arrives.
Expectation propagation - Expectation propagation, or EP, is an approximate Bayesian inference method that replaces difficult probability factors with simpler approximations and...
Experience replay - Experience replay is a training technique where a reinforcement learning agent stores its past experiences and repeatedly trains on random samples of them...
Extremal Ensemble Learning - Extremal Ensemble Learning, or EEL, is a research approach that iteratively updates an ensemble of candidate solutions using their relative quality.
Extremal optimization - Extremal optimization is a heuristic search method that preferentially changes poorly performing components of a candidate solution.
Factor graph - A factor graph represents how a function of several variables can be written as a product of smaller functions called factors.
Farthest-first traversal - Farthest-first traversal orders points by repeatedly selecting the point whose distance to its nearest previously selected point is greatest.
Feature - A feature is one measurable input field a model learns from, such as a customer's age, an order's total or the hour a call arrived.
Feature cross - A feature cross is a new input made by combining two or more existing fields, so the model can learn patterns that only appear when the fields interact.
Feature cutoff - A feature cutoff is a threshold that drops feature values below a set frequency or importance, keeping the input space small enough to be useful.
Feature engineering - Feature engineering is the work of choosing, transforming and inventing the input fields a model learns from, turning raw data into information the model...
Feature extraction - Feature extraction is deriving informative measurements from raw, unstructured material, such as turning a photo into edges and textures or a document into...
Feature extractor - A feature extractor is the component that converts raw input into feature values: the fixed recipe, pipeline stage or trained network that stands between...
Feature Freshness - Feature freshness is how up to date a model's input values are: the gap between what the data says and what is true right now.