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

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

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

order to make a prediction. Rule-based machine learning approaches include learning classifier systems, association rule learning, and artificial immune systems...

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

middle layer contains recurrent connections that change by a Hebbian learning rule.: 73–75 : Chapter 19, 21  Another model of associative memory is where...

Reinforcement learning is one of the three basic machine learning paradigms, alongside supervised learning and unsupervised learning. Q-learning at its simplest...

of learning language and communication, and the stage where a child begins to understand rules and symbols. This has led to a view that learning in organisms...

Q-learning is a model-free reinforcement learning algorithm that teaches an agent to assign values to each action it might take, conditioned on the agent...

In machine learning, the delta rule is a gradient descent learning rule for updating the weights of the inputs to artificial neurons in a single-layer...

middle layer contains recurrent connections that change by a Hebbian learning rule.: 73–75  Later, in Principles of Neurodynamics (1961), he described...

= ∑ j = 0 n x j w j {\displaystyle y=\sum _{j=0}^{n}x_{j}w_{j}} The learning rule used by ADALINE is the LMS ("least mean squares") algorithm, a special...

thought to be a substrate for Hebbian learning during development. As suggested by Taylor in 1973, Hebbian learning rules might create informationally efficient...

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...

backpropagation, unsupervised learning also employs other methods including: Hopfield learning rule, Boltzmann learning rule, Contrastive Divergence, Wake...

from are based on a consistent and simple rule. Both offline data collection models, where the model is learning by interacting with a static dataset and...

Multimodal learning is a type of deep learning that integrates and processes multiple types of data, referred to as modalities, such as text, audio, images...

Deep reinforcement learning (deep RL) is a subfield of machine learning that combines reinforcement learning (RL) and deep learning. RL considers the problem...

Deep learning is a subset of machine learning that focuses on utilizing neural networks to perform tasks such as classification, regression, and representation...

the value function for the current state using the rule: V ( S t ) ← ( 1 − α ) V ( S t ) + α ⏟ learning rate [ R t + 1 + γ V ( S t + 1 ) ⏞ The TD target...

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)...

A transformer is a deep learning architecture that was developed by researchers at Google and is based on the multi-head attention mechanism, which was...

weights of an Ising model by Hebbian learning rule as a model of associative memory, adding in the component of learning. This was popularized as the Hopfield...

In machine learning and pattern recognition, a feature is an individual measurable property or characteristic of a data set. Choosing informative, discriminating...

Decision tree learning is a supervised learning approach used in statistics, data mining and machine learning. In this formalism, a classification or...

learning rule for training neural networks, called the 'novelty rule', to help alleviate catastrophic interference. As its name suggests, this rule helps...

during the learning process. It was introduced by Donald Hebb in his 1949 book The Organization of Behavior. The theory is also called Hebb's rule, Hebb's...

In machine learning (ML), boosting is an ensemble metaheuristic for primarily reducing bias (as opposed to variance). It can also improve the stability...

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

The Journal of Machine Learning Research. 3: 1137–1155. Auer, Peter; Harald Burgsteiner; Wolfgang Maass (2008). "A learning rule for very simple universal...