Logic learning machine (LLM) is a machine learning method based on the generation of intelligible rules. LLM is an efficient implementation of the Switching...

borrowed from statistics, fuzzy logic, and probability theory. There is a close connection between machine learning and compression. A system that predicts...

neural network Long short-term memory (LSTM) Logic learning machine Self-organizing map Association rule learning Apriori algorithm Eclat algorithm FP-growth...

of Laws (Latin: Legum Magister), a postgraduate degree Logic learning machine, a machine learning method All pages with titles containing LLM LL (disambiguation)...

In logic, statistical inference, and supervised learning, transduction or transductive inference is reasoning from observed, specific (training) cases...

Machine Learning. 46: 225–254. doi:10.1023/A:1012470815092. Simon Colton and Stephen Muggleton (2006). "Mathematical Applications of Inductive Logic Programming"...

Rule-based machine learning (RBML) is a term in computer science intended to encompass any machine learning method that identifies, learns, or evolves...

Artificial Intelligence: Logic, Probability, and Computation", Synthesis Lectures on Artificial Intelligence and Machine Learning" March 2016 ISBN 9781627058414...

are considered to be possible values of the dependent variable. In machine learning, the observations are often known as instances, the explanatory variables...

by Stephen Muggleton in 1990, defined as the intersection of machine learning and logic programming. Muggleton and Wray Buntine introduced predicate invention...

In machine learning, semantic analysis of a text corpus is the task of building structures that approximate concepts from a large set of documents. It...

explainable AI (XAI), often overlapping with interpretable AI or explainable machine learning (XML), is a field of research that explores methods that provide humans...

Quantum machine learning (QML) is the study of quantum algorithms which solve machine learning tasks. The most common use of the term refers to quantum...

His fuzzy logic further provided a means for propagating combinations of these values through logical formulas. Symbolic machine learning approaches...

A Tsetlin machine is an artificial intelligence algorithm based on propositional logic. A Tsetlin machine is a form of learning automaton collective for...

In machine learning, deep learning focuses on utilizing multilayered neural networks to perform tasks such as classification, regression, and representation...

at the University of Washington. He is a researcher in machine learning known for Markov logic network enabling uncertain inference. Domingos received...

Transfer learning (TL) is a technique in machine learning (ML) in which knowledge learned from a task is re-used in order to boost performance on a related...

In machine learning, supervised learning (SL) is a type of machine learning paradigm where an algorithm learns to map input data to a specific output based...

Wayback Machine." NIPS. 2010. Lopez-Paz, David, et al. "Towards a learning theory of cause-effect inference Archived 13 March 2017 at the Wayback Machine" ICML...

Artificial intelligence and machine learning techniques are used in video games for a wide variety of applications such as non-player character (NPC) control...

Reinforcement learning is one of the three basic machine learning paradigms, alongside supervised learning and unsupervised learning. Reinforcement learning differs...

probabilistic machine learning: "An Inductive Inference Machine". See AI winter § Machine translation and the ALPAC report of 1966 Compared with symbolic logic, formal...

artificial intelligence and programming, which addresses learning of typically declarative (logic or functional) and often recursive programs from incomplete...

techniques like fuzzy logic (and "less robust" logic) can be applied to learning algorithms. Valiant essentially redefines machine learning as evolutionary...

logic and formal reasoning from antiquity to the present led directly to the invention of the programmable digital computer in the 1940s, a machine based...

The Master Algorithm: How the Quest for the Ultimate Learning Machine Will Remake Our World is a book by Pedro Domingos released in 2015. Domingos wrote...

Applying machine learning (ML) (including deep learning) methods to the study of quantum systems is an emergent area of physics research. A basic example...

knowledge is usually represented in a logic-based action description language and used as input for automated planners. Learning action models is important when...

In machine learning, a neural network (also artificial neural network or neural net, abbreviated ANN or NN) is a computational model inspired by the structure...