Parity learning - Parity learning is the task of learning which input bits are combined by an exclusive-or rule to produce a binary output.
Partial least squares path modeling - Partial least squares path modeling is a structural-modeling approach that estimates relationships among constructs using weighted combinations of observed...
Passive-aggressive learning - Passive-aggressive learning is a family of online learning methods that update a predictor as labeled examples arrive.
Path analysis (statistics) - Path analysis is a statistical method for estimating a specified network of relationships among observed variables.
Pattern recognition - Pattern recognition is the broad discipline of getting machines to spot regularities in data and act on them: classifying images, reading handwriting...
Pattern theory - Pattern theory is a mathematical framework, associated with Ulf Grenander, for describing complex patterns through simpler elements, their relationships...
Pedagogical agent - A pedagogical agent is a software-based character or interactive assistant designed to support learning.
Perceiver - Perceiver is a neural-network architecture that uses cross-attention to map a large input into a smaller latent array, then performs most processing within...
Physical artificial intelligence - Physical artificial intelligence is a broad term for AI used in systems that perceive, reason about or act within the physical world.
Pipeline - A pipeline is the chain of steps that turns raw inputs into a finished result, where each step's output feeds the next: collect, clean, transform, train...
Pipelining - Pipelining is the practice of overlapping the stages of work so that while one item is being processed by a later stage, the next item is already moving...
Plan-and-solve - Plan-and-solve is a prompting approach where the model is asked to plan its attack on a problem first, then execute the plan step by step, instead of...
Plate notation - Plate notation is a compact way to draw probabilistic graphical models with repeated structure.
Polysemanticity - Polysemanticity is the property of a neural-network component, often an individual neuron, responding to several distinct features or concepts rather than...
Positional encoding - Positional encoding is the information added to a transformer's inputs so it can tell where each token sits in the sequence, because the architecture itself...
Precomputed Features - Precomputed features are input variables calculated in advance and stored, so that training or serving reads them ready-made instead of recomputing them...
Prediction - A prediction is a model's answer for a case it was not told the answer to: the estimated price of a house, the class of an email, the demand for next month...
Predictive estimation - Predictive estimation is judging a model or a parameter choice by how well it predicts unseen data, rather than by how well it explains or fits the data...
Predictive ML - Predictive ML is the branch of machine learning that learns from historical examples to predict unknown outcomes: what will happen, which class a case...
Predictive state representation - A predictive state representation, or PSR, represents the state of a controlled dynamical system using predictions about future observations under specified...
Preference learning - Preference learning uses observed or stated preferences to learn how alternatives should be compared, ranked or selected.
Preference regression - Preference regression models preference or liking scores as a function of product attributes or perceptual dimensions.
PRESS statistic - The PRESS statistic, commonly expanded as prediction or predicted residual sum of squares, sums squared prediction errors obtained by leaving each...
Principal curve - A principal curve is a smooth one-dimensional curve that threads through the center of a cloud of data, generalizing the straight line of principal...
Prior belief - A prior belief is what a model or an analyst is prepared to believe before seeing the current data: the starting assumption that evidence then updates...
Proactive learning - Proactive learning is an extension of active learning that chooses both which example to label and which information source to ask.
Proaftn - PROAFTN is a fuzzy multicriteria classification method that assigns objects to predefined categories by comparing their characteristics with class...
Probabilistic numerics - Probabilistic numerics treats numerical computation as inference about an unknown mathematical quantity.
Probability matching - Probability matching is a choice pattern in which alternatives are selected in proportions resembling their perceived probabilities or observed frequencies.
Procedural memory - Procedural memory, in an AI agent, is the stored knowledge of how to do things: reusable routines, skills and rules for carrying out tasks, as distinct from...
Product of experts - A product of experts is a probabilistic model that combines several component models by multiplying their scores for the same possible outcome and then...
Programming by example - Programming by example is an approach to creating programs in which a user supplies examples of the desired behavior and a system infers a program...
Proper generalized decomposition - Proper generalized decomposition, or PGD, is a numerical modeling approach that approximates a solution as a sum of products of simpler functions.
Prototype methods - Prototype methods are machine-learning approaches that represent categories or patterns through selected or learned representative examples, called prototypes.
Proximal gradient methods for learning - Proximal gradient methods are optimization algorithms that combine a gradient step for a smooth part of an objective with a proximal step for another part...
Proxy labels - Proxy labels are stand-in labels used for training when the true outcome you care about cannot be observed directly, so a related, measurable quantity is...
Pure function - A pure function is a function whose output depends only on its inputs and which has no side effects: given the same inputs it always returns the same result...
PVLV - PVLV stands for primary value and learned value.
Q-function - A Q-function estimates how good it is to take a particular action in a particular situation, measured as the total reward an agent can expect to collect...
Quadratic classifier - A quadratic classifier assigns categories using a decision function that includes second-degree terms, such as squared features and products of pairs of...
Quadratic unconstrained binary optimization - Quadratic unconstrained binary optimization, usually abbreviated QUBO, is a problem formulation that minimizes a quadratic objective over variables...
Quantum artificial life - Quantum artificial life is a research area that constructs quantum models inspired by processes associated with living systems, such as inheritance...
Query-level feature - A query-level feature is a measurable property of a search query, rather than a property of an individual candidate document or of the match between the two.
Quickprop - Quickprop is a neural-network optimization algorithm introduced by Scott Fahlman that uses successive gradient information to estimate larger, useful weight...
Radial Basis Function regression - Radial Basis Function regression is a regression method that predicts a value as a weighted sum of radial basis functions, each one centered on a training...