6.6 KiB
| title | chunk | source | category | tags | date_saved | instance |
|---|---|---|---|---|---|---|
| Glossary of artificial intelligence | 9/21 | https://en.wikipedia.org/wiki/Glossary_of_artificial_intelligence | reference | science, encyclopedia | 2026-05-05T07:50:25.401446+00:00 | kb-cron |
error-driven learning A sub-area of machine learning concerned with how an agent ought to take actions in an environment so as to minimize some error feedback. It is a type of reinforcement learning.
ensemble learning The use of multiple machine learning algorithms to obtain better predictive performance than could be obtained from any of the constituent learning algorithms alone.
epoch In machine learning, particularly in the creation of artificial neural networks, an epoch is training the model for one cycle through the full training dataset. Small models are typically trained for as many epochs as it takes to reach the best performance on the validation dataset. The largest models may train for only one epoch.
ethics of artificial intelligence The part of the ethics of technology specific to artificial intelligence.
evolutionary algorithm (EA) A subset of evolutionary computation, a generic population-based metaheuristic optimization algorithm. An EA uses mechanisms inspired by biological evolution, such as reproduction, mutation, recombination, and selection. Candidate solutions to the optimization problem play the role of individuals in a population, and the fitness function determines the quality of the solutions (see also loss function). Evolution of the population then takes place after the repeated application of the above operators.
evolutionary computation A family of algorithms for global optimization inspired by biological evolution, and the subfield of artificial intelligence and soft computing studying these algorithms. In technical terms, they are a family of population-based trial and error problem solvers with a metaheuristic or stochastic optimization character.
evolving classification function (ECF) Evolving classification functions are used for classifying and clustering in the field of machine learning and artificial intelligence, typically employed for data stream mining tasks in dynamic and changing environments.
existential risk from artificial general intelligence The hypothesis that substantial progress in artificial general intelligence (AGI) could someday result in human extinction or some other unrecoverable global catastrophe.
expert system A computer system that emulates the decision-making ability of a human expert. Expert systems are designed to solve complex problems by reasoning through bodies of knowledge, represented mainly as if–then rules rather than through conventional procedural code.
== F ==
fast-and-frugal trees A type of classification tree. Fast-and-frugal trees can be used as decision-making tools which operate as lexicographic classifiers, and, if required, associate an action (decision) to each class or category.
feature An individual measurable property or characteristic of a phenomenon. In machine learning, a feature is often an input variable or attribute used by a model to represent an observation for tasks such as prediction, classification, and pattern recognition. In computer vision and image processing, a feature is a piece of information about the content of an image; typically about whether a certain region of the image has certain properties. Features may be specific structures in an image (such as points, edges, or objects), or the result of a general neighborhood operation or feature detection applied to the image.
feature extraction In machine learning, pattern recognition, and image processing, feature extraction starts from an initial set of measured data and builds derived values (features) intended to be informative and non-redundant, facilitating the subsequent learning and generalization steps, and in some cases leading to better human interpretations.
feature learning
Also representation learning. In machine learning, feature learning or representation learning is a set of techniques that allows a system to automatically discover the representations needed for feature detection or classification from raw data. This replaces manual feature engineering and allows a machine to both learn the features and use them to perform a specific task.
feature selection In machine learning and statistics, feature selection, also known as variable selection, attribute selection or variable subset selection, is the process of selecting a subset of relevant features (variables, predictors) for use in model construction.
federated learning A machine learning technique that allows for training models on multiple devices with decentralized data, thus helping preserve the privacy of individual users and their data.
first-order logic
Also first-order predicate calculus or predicate logic. A collection of formal systems used in mathematics, philosophy, linguistics, and computer science. First-order logic uses quantified variables over non-logical objects and allows the use of sentences that contain variables, so that rather than propositions such as Socrates is a man one can have expressions in the form "there exists X such that X is Socrates and X is a man" and there exists is a quantifier while X is a variable. This distinguishes it from propositional logic, which does not use quantifiers or relations.
fluent A condition that can change over time. In logical approaches to reasoning about actions, fluents can be represented in first-order logic by predicates having an argument that depends on time.
formal language A set of words whose letters are taken from an alphabet and are well-formed according to a specific set of rules.
forward chaining
Also forward reasoning. One of the two main methods of reasoning when using an inference engine and can be described logically as repeated application of modus ponens. Forward chaining is a popular implementation strategy for expert systems, businesses and production rule systems. The opposite of forward chaining is backward chaining. Forward chaining starts with the available data and uses inference rules to extract more data (from an end user, for example) until a goal is reached. An inference engine using forward chaining searches the inference rules until it finds one where the antecedent (If clause) is known to be true. When such a rule is found, the engine can conclude, or infer, the consequent (Then clause), resulting in the addition of new information to its data.
frame An artificial intelligence data structure used to divide knowledge into substructures by representing "stereotyped situations". Frames are the primary data structure used in artificial intelligence frame language.