💡 Words with a Similar Meaning to "Association rule learning"
Found via reverse dictionary — words that share a conceptual meaning.
| Word | Definition |
|---|---|
| feature learning | In machine learning, feature learning or representation learning is a set of techniques that allow a system to automatically discover the representations needed for feature detection or classification from raw data. |
| decision tree learning | a supervised learning approach used in statistics, data mining and machine learning. |
| statistical learning theory | a framework for machine learning drawing from the fields of statistics and functional analysis. |
| supervised learning | In machine learning, supervised learning is a paradigm where a model is trained using input objects (e.g. a vector of predictor variables) and desired output values (also known as a supervisory signal), which are often human-made labels. |
| bayesian knowledge tracing | — |
| knowledge graph embedding | In representation learning, knowledge graph embedding, also referred to as knowledge representation learning, or multi-relation learning, is a machine learning task of learning a low-dimensional representation of a knowledge graph's entities and relations while preserving their semantic meaning. |
| affinity analysis | Affinity analysis falls under the umbrella term of data mining which uncovers meaningful correlations between different entities according to their co-occurrence in a data set. |
| label propagation algorithm | Label propagation is a semi-supervised machine learning algorithm that assigns labels to previously unlabeled data points. |
| large margin nearest neighbor | Large margin nearest neighbor classification is a statistical machine learning algorithm for metric learning. |
| relational algebra | In database theory, relational algebra is a theory that uses algebraic structures for modeling data and defining queries on it with well founded semantics. |
| probabilistic neural network | A probabilistic neural network is a feedforward neural network, which is widely used in classification and pattern recognition problems. |
| sequence labeling | In machine learning, sequence labeling is a type of pattern recognition task that involves the algorithmic assignment of a categorical label to each member of a sequence of observed values. |
| deep reinforcement learning | (deep RL) a subfield of machine learning that combines reinforcement learning and deep learning. |
| linear classifier | In machine learning, a linear classifier makes a classification decision for each object based on a linear combination of its features. |
| dynamic causal modeling | a framework for specifying models, fitting them to data and comparing their evidence using Bayesian model comparison. |
| exploratory factor analysis | In multivariate statistics, exploratory factor analysis is a statistical method used to uncover the underlying structure of a relatively large set of variables. |
| structured prediction | Structured prediction or structured output learning is an umbrella term for supervised machine learning techniques that involves predicting structured objects, rather than discrete or real values. |
| log-linear analysis | a technique used in statistics to examine the relationship between more than two categorical variables. |
| gradient boosting | a machine learning technique based on boosting in a functional space, where the target is pseudo-residuals instead of residuals as in traditional boosting. |
| relevance vector machine | In mathematics, a Relevance Vector Machine is a machine learning technique that uses Bayesian inference to obtain parsimonious solutions for regression and probabilistic classification. |
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