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

prediction. Rule-based machine learning approaches include learning classifier systems, association rule learning, and artificial immune systems. Based on the...

Learning classifier systems, or LCS, are a paradigm of rule-based machine learning methods that combine a discovery component (e.g. typically a genetic...

Association rule learning is a rule-based machine learning method for discovering interesting relations between variables in large databases. It is intended...

Expert systems Rewriting RuleML List of rule-based languages Learning classifier system Rule-based machine learning Rule-based modeling Crina Grosan; Ajith...

Artificial immune systems (AIS) are a class of rule-based machine learning systems inspired by the principles and processes of the vertebrate immune system...

algorithm Reinforcement learning Repeated incremental pruning to produce error reduction (RIPPER) Rprop Rule-based machine learning Skill chaining Sparse...

raw dataset to building a machine learning model ready for deployment. AutoML was proposed as an artificial intelligence-based solution to the growing challenge...

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

An artificial neural network's learning rule or learning process is a method, mathematical logic or algorithm which improves the network's performance...

In machine learning, reinforcement learning from human feedback (RLHF) is a technique to align an intelligent agent with human preferences. It involves...

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 (ML), boosting is an ensemble learning method that combines a set of less accurate models (called "weak learners") to create a single...

In machine learning, attention is a method that determines the importance of each component in a sequence relative to the other components in that sequence...

Active learning is a special case of machine learning in which a learning algorithm can interactively query a human user (or some other information source)...

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

page is a timeline of machine learning. Major discoveries, achievements, milestones and other major events in machine learning are included. History of...

Adversarial machine learning is the study of the attacks on machine learning algorithms, and of the defenses against such attacks. A survey from May 2020...

In machine learning, support vector machines (SVMs, also support vector networks) are supervised max-margin models with associated learning algorithms...

In statistics and machine learning, ensemble methods use multiple learning algorithms to obtain better predictive performance than could be obtained from...

Q-learning is a reinforcement learning algorithm that trains an agent to assign values to its possible actions based on its current state, without requiring...

trees. Machine Learning, 4(2): 161-186, 1989 Ferrer-Troyano, Francisco, Jesus S. Aguilar-Ruiz, and Jose C. Riquelme. Incremental rule learning based on example...

Propagation of schema Universal Darwinism Metaheuristics Learning classifier system Rule-based machine learning Pétrowski, Alain; Ben-Hamida, Sana (2017). Evolutionary...

Statistical learning theory is a framework for machine learning drawing from the fields of statistics and functional analysis. Statistical learning theory...

In machine learning, diffusion models, also known as diffusion-based generative models or score-based generative models, are a class of latent variable...

In deep learning, transformer is an architecture based on the multi-head attention mechanism, in which text is converted to numerical representations called...

Meta-learning is a subfield of machine learning where automatic learning algorithms are applied to metadata about machine learning experiments. As of...

In machine learning, normalization is a statistical technique with various applications. There are two main forms of normalization, namely data normalization...

Mixture of experts (MoE) is a machine learning technique where multiple expert networks (learners) are used to divide a problem space into homogeneous...

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