Associative classifier - An associative classifier is a supervised learning model that predicts a class using association rules.
Attributional calculus - Attributional calculus is a logic and knowledge-representation language developed by Ryszard S.
Audio inpainting - Audio inpainting is the reconstruction of missing or damaged parts of an audio signal using information from the available recording and a model of...
Augmented reality - Augmented reality, or AR, overlays computer-generated images, text or information onto a live view of the real world, usually through a phone, tablet or...
Autognostics - Autognostics is an uncommon term used in discussions of autonomic networking for a network's ability to monitor and diagnose its own condition and use that...
Automated negotiation - Automated negotiation is a process in which software agents exchange, assess or revise proposals to seek an agreement under defined rules.
Automatic Speech Recognition - Automatic Speech Recognition, or ASR, is technology that converts spoken audio into written text, so that machines can transcribe, search or act on what...
Autonomic networking - Autonomic networking is an approach in which network functions manage aspects of their own configuration, optimization, protection or recovery.
Autoregressive Hidden Markov Model - An autoregressive hidden Markov model is a hidden Markov model in which each observation depends not only on the hidden state but also on the previous...
Auxiliary loss - An auxiliary loss is an extra loss term added to a model's training objective, alongside the main loss, to give additional learning signal and help the...
Auxiliary Tasks - Auxiliary tasks are extra learning tasks, related to the main one, that a model is trained on at the same time, to help it learn representations that...
Averaged one-dependence estimators - Averaged one-dependence estimators, or AODE, is a probabilistic classification method that averages a constrained collection of models allowing limited...
Axis-aligned condition - An axis-aligned condition is a test in a decision tree that looks at one feature only, such as age is below 30 or temperature is above 25, splitting the...
Backfitting - Backfitting is an iterative method for fitting additive models: it cycles through the model's components, refitting each one to the residuals left by the...
Bag of words - A bag of words is a way of representing text by counting which words appear in it and how often, ignoring grammar and word order.
Bagging - Bagging, short for bootstrap aggregating, is an ensemble method that trains many models on different random resamples of the training data and combines...
Ball tree - A ball tree is a data structure that organizes points into nested regions defined by a center and a radius.
Base rate - A base rate is the underlying frequency or probability of an event in a relevant population before considering a particular test result or signal.
Basis function regression - Basis function regression is a way of fitting curved relationships by transforming an input into a set of fixed building-block functions, such as powers or...
Batch - In machine learning, a batch is a group of training examples processed together in one step before the model updates its parameters.
Batch size - Batch size is the number of training examples a model works through before it makes one adjustment to itself, a setting that trades speed, memory use and...
Bayes factor - A Bayes factor compares how well two specified models predict observed data.
Bayesian information criterion - The Bayesian information criterion, or BIC, is a model-selection score that balances fit to observed data against model complexity.
Bayesian interpretation of kernel regularization - The Bayesian interpretation of kernel regularization explains how certain penalized kernel-learning methods correspond to probabilistic models with priors.
Bayesian linear regression - Bayesian linear regression is a method for predicting a numerical outcome with a linear model while expressing uncertainty about its coefficients and other...
Bayesian multivariate linear regression - Bayesian multivariate linear regression jointly models several numerical outcomes using linear relationships with predictors.
Bayesian programming - Bayesian programming is an approach to specifying computational problems through probability models and answering questions by probabilistic inference.
Bayesian regret - Bayesian regret is expected regret averaged over a prior distribution of possible environments or unknown parameters.
Bayesian Ridge Regression - Bayesian ridge regression is linear regression with a Gaussian prior on the coefficients, producing a probability distribution over those coefficients.
Bayesian structural time series - Bayesian structural time series, or BSTS, is a family of models that represents a time series through components such as trend, seasonality and regression...
Behavioral clustering - Behavioral clustering groups entities according to similarities in observed behavior.
Bidirectional - In AI, bidirectional describes a model that reads a sequence in both directions at once, from start to end and from end to start, so each word or item is...
Bigram - A bigram is a pair of neighboring items in a sequence, usually two adjacent words such as New York or not good, counted or predicted together so a little...
Binary classification - Binary classification is the task of sorting items into exactly two categories, such as spam or not spam, churn or stay, approve or decline.
Binary condition - A binary condition is a yes-or-no test, such as order value above 100 or country is France, used at each node of a decision tree or rule set to send every...
Boosting - Boosting is an ensemble method that trains models one after another, each new model concentrating on the mistakes the earlier ones made, and combines them...
Broadcasting - Broadcasting is the set of rules that lets software apply one operation across arrays of different shapes, such as subtracting a column average from every...
Brown clustering - Brown clustering is an unsupervised method that groups words into a hierarchy using patterns of neighboring words in a text collection.
Bucketing - Bucketing groups values into ranges, or buckets, such as ages 18 to 24 and 25 to 34, so a model or report works with a few clear groups instead of thousands...
C4.5 algorithm - C4.5 is a supervised learning algorithm developed by Ross Quinlan for building classification decision trees from labeled examples.
Calibration curves - A calibration curve checks whether a model's confidence means what it says: when the model predicts a 70 percent chance for a group of cases, about 70...
Candidate generation - Candidate generation is the fast first stage of a recommendation or search system, which picks a few hundred plausible items out of millions so a slower...
Canonical correspondence analysis - Canonical correspondence analysis is a constrained ordination method that relates a table of species abundances, or similar non-negative response data, to...
Cascading classifiers - Cascading classifiers are classification stages arranged so that an example's result at one stage determines whether it proceeds to another.
Case-based reasoning - Case-based reasoning is an approach to problem solving that uses relevant past cases to help address a new situation.
Categorical data - Categorical data is data that names a group or label rather than measuring an amount, such as country, product type, plan level or payment method.
Category utility - Category utility is a score for judging how informative a grouping is.