Acquiescence bias - Acquiescence bias is a response tendency to agree with statements regardless of their specific content.
Action - In AI, an action is anything an agent can do that changes its environment or its relationship to it: moving, sending, writing, booking, buying.
Action cost function - An action cost function assigns a numerical cost to taking an action in a planning or search problem.
Action model learning - Action model learning is the process of learning descriptions of what actions require and how they change an environment.
Action monitoring - Action monitoring is the practice of checking whether the conditions required for the next planned action still hold before attempting it, and detecting...
Action schema - An action schema is a parameterized description of a type of action in an AI planning model.
Action space - An action space is the set of actions an agent can choose from in a decision-making environment.
Activation - In a neural network, an activation is a numerical value produced by a unit or layer for a particular input, commonly after applying an activation function...
Activation function - An activation function transforms a neural network's intermediate values before they pass onward or become outputs.
Activation Layer - An activation layer is a neural-network component that applies an activation function to incoming values.
Active learning - Active learning is a machine-learning approach where the system chooses which examples it wants labeled next, instead of learning from a fixed or random set.
Active learning agent - An active learning agent is a system that improves itself by choosing which examples it wants to be taught, instead of passively accepting whatever data it...
Active-set method - An active-set method is an optimization approach that works with a changing subset of constraints believed to be tight at the solution.
Activity Bias - Activity bias occurs when data or conclusions disproportionately reflect highly active users while less active or inactive users are poorly represented.
Activity expressions - Activity expressions describe situations that unfold over time without building a particular completion point into their meaning.
Actor - An actor, in planning, is an entity that performs actions within the modeled environment.
Actor-Critic Methods - Actor-critic methods are reinforcement-learning methods that learn both a policy, called the actor, and a value estimate, called the critic.
Acyclic directed mixed graph - An acyclic directed mixed graph, or ADMG, contains directed edges such as X → Y and bidirected edges such as X ↔ Y, with no cycle made entirely of...
Ad hoc retrieval - Ad hoc retrieval is the information-retrieval task of finding documents relevant to a user's current information need in response to a query.
Ad hoc survey - An ad hoc survey is a survey undertaken without a plan to repeat it.
ADALINE - ADALINE is an early artificial neural model whose name is commonly expanded as adaptive linear neuron or adaptive linear element.
Adam - Adam is the most widely used algorithm for training neural networks: an optimization method that adjusts each of the model's millions of internal numbers...
Adamic - Adar index - The Adamic - Adar index is a graph similarity score that adds up contributions from the neighbors shared by two nodes, giving more weight to shared...
Adaptive boosting - Adaptive boosting, or AdaBoost, is an ensemble method that combines many simple, weak models into one strong predictor by training them in sequence, each...
Adaptive compression - Adaptive compression is compression that changes its model or coding decisions in response to the data being processed.
Adaptive design (medicine) - An adaptive design in medicine is a clinical-trial design that permits specified changes based on accumulating trial data according to a plan established in...
Adaptive differential pulse-code modulation - Adaptive differential pulse-code modulation (ADPCM) is a family of audio-coding methods that represents the difference between a sample and its predicted...
Adaptive dynamic programming - Adaptive dynamic programming is a model-based reinforcement-learning approach that learns how an environment behaves from experience and uses dynamic...
Adaptive filtering - Adaptive filtering uses a filter whose parameters change in response to observed data or an error signal.
Adaptive histogram equalization - Adaptive histogram equalization, or AHE, is an image contrast method that uses the brightness distribution around each location to adjust pixel values.
Adaptive Homogeneity-Directed (AHD) demosaicking - Adaptive Homogeneity-Directed demosaicking, usually abbreviated AHD, is a method for reconstructing full-color images from a color filter array.
Adaptive Huffman coding - Adaptive Huffman coding is lossless compression that changes a Huffman code tree as symbols are processed.
Adaptive learning - Adaptive learning, in the machine-learning sense used here, updates a predictive model during operation so that it can respond to changing patterns in...
Adaptive Learning Rate Methods - Adaptive learning rate methods are training algorithms that give each parameter of a model its own automatically adjusted step size during learning, instead...
Adaptive Markov chain Monte Carlo - Adaptive Markov chain Monte Carlo, or adaptive MCMC, changes aspects of a sampling algorithm using information gathered during its run.
Adaptive neuro fuzzy inference system - An adaptive neuro fuzzy inference system, commonly called ANFIS, combines fuzzy rules with a trainable network structure.
Adaptive redaction - Adaptive redaction is the policy-directed removal or replacement of selected sensitive content according to the circumstances of a communication or data use.
Adaptive rejection sampling - Adaptive rejection sampling, or ARS, draws from a target distribution by constructing and refining an upper envelope used for rejection sampling.
Adaptive resampling - Adaptive resampling, in sequential Monte Carlo and particle filtering, means deciding whether to resample a population of weighted particles using its...
Adaptive resonance theory - Adaptive resonance theory, or ART, is a theory and family of neural models concerned with learning new patterns while preserving useful earlier knowledge.
Adaptive response rate - Adaptive response rate describes a forecasting approach that changes how strongly it reacts to new observations according to evidence of changing conditions.
Adaptive sampling - Adaptive sampling is an approach in which information from earlier samples influences which samples are selected next or how sampling effort is allocated.
Adaptive sharpening - Adaptive sharpening increases the apparent clarity of image detail while varying its strength according to local image conditions.
Adaptive softmax - Adaptive softmax is a hierarchical output-layer method for efficiently modeling large, unevenly used vocabularies.
Adaptive Synthetic Sampling - Adaptive Synthetic Sampling, usually abbreviated ADASYN, is an oversampling method for imbalanced classification.
Adaptive utility - Adaptive utility is a decision-theoretic approach in which uncertainty about preferences can be reduced through experience or information.
Add gate - An add gate is the learned control in an LSTM network that regulates how much newly proposed information enters its memory state.
Add-k - Add-k is a probability-smoothing method that adds the same positive amount k to every possible outcome's observed count before normalizing.