Supervised learning - Supervised learning trains a model using examples that pair inputs with known target outputs.
Support vector machine - A support vector machine, or SVM, is a learning method that can build a decision boundary between classes while balancing separation against errors.
SUPS - SUPS stands for synaptic updates per second, a throughput measure used in neural simulation and some neural computing contexts.
Surrogate model - A surrogate model is a simpler or cheaper model used to approximate the behavior of another system, model, experiment, or computational process.
Switching Hidden Markov Model - A switching hidden Markov model describes a sequence by switching among the outputs of several hidden Markov models.
Symbol level - The symbol level is a way of describing a computing system in terms of its representations and the operations that manipulate them.
Symbolic artificial intelligence - Symbolic artificial intelligence is an approach to AI that represents information using explicit symbols and structured expressions, then applies defined...
Symbolic regression - Symbolic regression is a machine learning task that searches for mathematical expressions that fit data.
Syntactic pattern recognition - Syntactic pattern recognition identifies or interprets patterns through the arrangement of simpler parts and rules describing valid structures.
Synthetic feature - A synthetic feature is a model input constructed from other available inputs.
Target network - A target network is a more slowly updated network used to calculate learning targets in some reinforcement-learning methods.
Targeted maximum likelihood estimation - Targeted maximum likelihood estimation, or TMLE, is a statistical framework that updates an initial estimate specifically to improve estimation of a chosen...
Task decomposition - Task decomposition means dividing a larger goal into smaller, executable tasks.
Temperature Scaling - Temperature scaling is a method for adjusting a classifier's probability estimates by dividing its logits by a learned positive number before applying softmax.
Temporal data - Temporal data is data associated with different points or periods in time.
Tensor sketch - Tensor sketch is a randomized technique for representing a large tensor-product feature map with a much smaller vector.
Termination condition - A termination condition is a rule that determines when a process stops.
Test set - A test set is a collection of examples reserved for assessing a model after its development choices have been made.
Text classification - Text classification assigns one or more categories to a piece of text.
Text span - A text span is a specific continuous portion of text identified by its start and end positions.
Three-factor learning - Three-factor learning is a family of synaptic learning rules in which changes depend on presynaptic activity, postsynaptic activity and an additional...
Time travel (for features) - Time travel for features is the ability to retrieve historical versions of model-input values.
Timestep - A timestep is one position in a sequential calculation.
Totally Random Trees Embedding - Totally random trees embedding is an unsupervised method that represents an input by the leaves it reaches in a collection of randomly constructed trees.
Tower - A tower is a substantial neural-network component within a larger neural network.
Training - Training is the process of adjusting a machine-learning model using data or experience.
Training set - A training set is the collection of examples used to fit a model's learned parameters.
Transduction - Transduction is learning or inference directed at a particular supplied collection of cases, rather than producing a general prediction rule for arbitrary...
Transfer learning - Transfer learning reuses knowledge learned in one task or setting to help solve another.
Tree-based feature selection - Tree-based feature selection uses a fitted decision tree or tree ensemble to help choose which input features a model should retain.
Trigram - A trigram is a sequence of three consecutive units, usually words or characters.
Triplet loss - Triplet loss is a training objective that encourages an anchor example to be closer to a related positive example than to an unrelated negative example by a...
Tsetlin machine - A Tsetlin machine is a learning model that uses teams of Tsetlin automata to construct propositional-logic clauses.
Tucker decomposition - Tucker decomposition represents a multiway array, or tensor, using a smaller core tensor and one factor matrix for each mode.
Ugly duckling theorem - The ugly duckling theorem is a result associated with Satosi Watanabe showing that, if all possible predicates are treated equally in a particular logical...
Uncertain data - Uncertain data are data whose values, identities, relationships or existence are not known with complete confidence.
Underfitting - Underfitting occurs when a model fails to capture useful patterns in its training data and consequently performs poorly.
Uniform convergence in probability - Uniform convergence in probability means that the largest error across an entire indexed collection of random quantities becomes small with high probability...
Univariate feature selection - Univariate feature selection evaluates input features one at a time and selects those that score well under a chosen statistical criterion.
Universal portfolio algorithm - The universal portfolio algorithm is an online portfolio-selection method introduced by Thomas Cover.
Universal psychometrics - Universal psychometrics is a research approach to measuring cognitive abilities across different kinds of systems, including humans, other animals...
Unlabeled data - Unlabeled data is a collection of examples without known target answers for the learning task being considered.
Unlabeled example - An unlabeled example is one record containing input information without a known target answer for the task at hand.
Unsupervised Clustering - Unsupervised clustering groups examples by patterns in their inputs without using predefined target labels to determine the groups.
Unsupervised learning - Unsupervised learning finds structure in data without using supplied target labels to guide the fitting objective.
Unsupported-claim rate - Unsupported-claim rate is the proportion of evaluated claims in a response that lack adequate grounding under a defined evidence standard.
Upweighting - Upweighting increases the contribution of selected examples to a calculation or learning objective.