RAMnets - RAMnets are neural-style pattern-recognition systems built from memory lookup units rather than conventional connections with continuously adjusted...
Rand index - The Rand index is a score between zero and one that measures how much two clusterings of the same items agree, by counting the pairs of items they treat the...
Random feature - A random feature is a component of a representation constructed using sampled parameters rather than learning every transformation parameter from the...
Random forest - A random forest is a prediction model made of many decision trees trained on different random slices of the data, whose answers are combined by voting or...
Random indexing - Random indexing is a method for building compact vector representations by assigning sparse random vectors to contexts and accumulating those vectors for...
Random subspace method - The random subspace method is an ensemble-learning approach in which different models are trained on randomly selected subsets of the available features.
Rank (ordinality) - Rank, or ordinality, is the property of data that has a meaningful order but no meaningful spacing: first, second and third tell you who beats whom, not by...
Ranking - Ranking is the task of ordering items by relevance, preference or predicted value, so that the best option comes first, rather than assigning each item an...
Re-ranking - Re-ranking is a second pass that takes the shortlist produced by a fast first ranking and reorders it with a more expensive, more careful method before...
Real-Time Machine Learning - Real-time machine learning is the practice of making predictions on live data within tight time budgets, usually milliseconds to seconds, so the answer...
Reasoning system - A reasoning system is a computational system that derives conclusions, explanations or decisions from represented information using defined inference...
Reciprocal human machine learning - Reciprocal human machine learning, or RHML, is an approach to human - AI collaboration designed so that both the person and the machine improve through...
Recursive feature elimination - Recursive feature elimination is a feature selection method that trains a model, drops the features it finds least useful, trains again, and repeats until...
Recursive self-improvement - Recursive self-improvement is a process in which an AI system contributes to improving itself, and the resulting version participates in further improvement...
Regression fallacy - The regression fallacy is the mistake of attributing a change after an unusually high or low observation entirely to an intervention while overlooking...
Regressor chains - A regressor chain is a method for predicting several numerical outputs by arranging individual regression models in an ordered sequence.
Regularization - Regularization is any deliberate penalty added to training that discourages an overly complicated model, trading a slightly worse fit on past data for a...
Regularization function - A regularization function is the specific mathematical penalty term added to a training objective that quantifies how complex a model is, turning keep it...
Regularization rate - The regularization rate is the dial that sets how strongly complexity is penalized during training: higher values force simpler models, lower values let the...
Relational data mining - Relational data mining discovers patterns involving linked entities and relationships, often across multiple tables in a relational database.
Relational dependency network - A relational dependency network, or RDN, is a probabilistic graphical model that represents dependencies among attributes of linked objects using a...
Relationship square - A relationship square is a plot that shows how strongly variables are associated with two selected dimensions from a factor analysis.
Relief (feature selection) - Relief is a feature-weighting and selection algorithm that estimates a feature's usefulness by comparing nearby labeled examples.
Reparameterization trick - The reparameterization trick rewrites a random sample as a deterministic function of model parameters and parameter-independent noise.
Replay buffer - A replay buffer is a memory of past experiences that a reinforcement learning agent stores and re-practices on, so each hard-won lesson is learned from many...
Replica - A replica is an identical running copy of a model or service, deployed alongside others so the system can serve more requests at once and survive the...
Representation - A representation is the form in which data is presented to a model: the encoding of a customer, an image or a sentence as numbers the algorithm can work...
Representation collapse - Representation collapse occurs when a learned representation loses distinctions needed to describe its inputs.
Return - In reinforcement learning, the return is the total reward an agent collects from a point onward, usually with future rewards discounted, and it is the...
Robot learning - Robot learning is the use of data and experience to help a robot acquire or improve capabilities.
Robust principal component analysis - Robust principal component analysis refers to methods that seek underlying low-dimensional structure while reducing sensitivity to substantial errors or...
Root - In tree-based models and tree-structured data, the root is the single starting node at the top, from which every branch, split and leaf descends; in a...
Rprop - Rprop, short for resilient backpropagation, is an optimization method that adjusts a separate update size for each trainable parameter using changes in the...
Rule-based machine learning - Rule-based machine learning uses rules as a central part of a model learned or adapted from data.
Rule induction - Rule induction is the process of learning explicit rules from observed examples.
Rules extraction system family - The rules extraction system family, commonly called the RULES family, is a group of related inductive-learning algorithms that derive explicit if-then rules...
Sammon mapping - Sammon mapping is a nonlinear dimensionality-reduction method that places observations in a lower-dimensional space while trying to preserve their pairwise...
Scalar - A scalar is a single number, as opposed to a list, table or grid of numbers; it carries one value with no direction or structure attached.
Scaling - Scaling is rescaling numeric features to comparable ranges, so that no variable dominates a model merely because of the units it happens to be measured in.
Schema-agnostic databases - Schema-agnostic databases, in the query-interface sense, let users request information without first knowing the database's exact structure or vocabulary.
Scoring - Scoring is applying a trained model to new cases to produce its outputs: a probability, a prediction, a risk grade, the number the model was built to give...
Self-correction - Self-correction is a system's ability to detect problems in its own output and revise it, either within one run through reflection and checking, or across...
Self-healing test automation - Self-healing test automation is test software that attempts to recover from certain changes or failures in its execution setup.
Self-management (computer science) - Self-management in computer science is the ability of a computing system to monitor and adjust aspects of its own operation under defined objectives and...
Self-supervised learning - Self-supervised learning is training where the data itself provides the labels: the model is asked to predict a hidden or rearranged part of the input, so...
Self-training - Self-training is a semi-supervised method where a model trained on a small labeled set labels the unlabeled data itself, then re-trains on its own most...
Semantic feature - A semantic feature is a feature that captures meaning, what a piece of text, an image or a record is about, rather than surface form like exact words...
Semantic folding - Semantic folding is a language-representation approach that encodes words and text as sparse binary patterns using a learned semantic reference space.