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 data mining and association rule learning, lift is a measure of the performance of a targeting model (association rule) at predicting or classifying...

Apriori is an algorithm for frequent item set mining and association rule learning over relational databases. It proceeds by identifying the frequent...

Unsupervised learning Expectation-maximization algorithm Vector Quantization Generative topographic map Information bottleneck method Association rule learning algorithms...

Anomaly/outlier/change detection Association rule learning Bayesian networks Classification Cluster analysis Decision trees Ensemble learning Factor analysis Genetic...

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

to the frequent pattern discovery approach that underlies most association rule learning techniques. Frequent pattern discovery techniques find all patterns...

reduce the search space for the problem. The support metric in the association rule learning algorithm is defined as the frequency of the antecedent or consequent...

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...

network Relational Markov network Relational Kalman filtering Association rule learning Formal concept analysis Fuzzy logic Grammar induction Knowledge...

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

Contrast set learning is a form of association rule learning that seeks to identify meaningful differences between separate groups by reverse-engineering...

recommendation systems For the most part, FP discovery can be done using association rule learning with particular algorithms Eclat, FP-growth and the Apriori algorithm...

processing algorithms and itemset mining which is typically based on association rule learning. Local process models extend sequential pattern mining to more...

tools are machine learning libraries for Python, like scikit-learn. Some major rule induction paradigms are: Association rule learning algorithms (e.g....

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

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...

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...

p(x|y)p(y)} by Bayes' rule. Semi-supervised learning with generative models can be viewed either as an extension of supervised learning (classification plus...

In machine learning (ML), feature learning or representation learning is a set of techniques that allow a system to automatically discover the representations...

Self-supervised learning (SSL) is a paradigm in machine learning where a model is trained on a task using the data itself to generate supervisory signals...

Eclat Textile, a Taiwanese textile company Lotus Eclat, a car Association rule learning § Eclat algorithm, an algorithm This disambiguation page lists...

Li, Haosong; Sheu, Phillip C.-Y. (2022-03-28). "A scalable association rule learning and recommendation algorithm for large-scale microarray datasets"...

In machine learning and statistics, the learning rate is a tuning parameter in an optimization algorithm that determines the step size at each iteration...

Curriculum learning is a technique in machine learning in which a model is trained on examples of increasing difficulty, where the definition of "difficulty"...

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)...

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...

In machine learning, the perceptron is an algorithm for supervised learning of binary classifiers. A binary classifier is a function that can decide whether...