Landmarks - Landmarks are distinctive, locatable points used as reference markers: in vision, named positions on faces or objects; in search and planning, well-known...
Large margin nearest neighbor - Large margin nearest neighbor, or LMNN, is a supervised metric-learning method that learns a distance transformation for nearest-neighbor classification.
LARS Lasso - LARS Lasso is the lasso fitted by the Least Angle Regression algorithm: an efficient method that traces the whole path of models as the penalty relaxes...
Lasso - Lasso is regression with an L1 penalty, famous for driving the weights of unneeded fields to exactly zero, so fitting the model and selecting its fields...
Latent class model - A latent class model represents a population as a mixture of unobserved categories, using patterns in observed variables to estimate those categories and...
Lazy learning - Lazy learning is a learning approach that postpones substantial generalization until a prediction or query is requested.
Leabra - Leabra is a computational neural-modeling framework that combines error-driven and associative learning with biologically motivated activation and...
Leaf - A leaf is an end node of a decision tree: the terminal box where a case finally lands, carrying the tree's prediction for every case that arrives there.
Learnable evolution model - The learnable evolution model, or LEM, is an evolutionary optimization approach that uses machine learning to generate promising new candidate solutions.
Learnable function class - A learnable function class is a set of candidate functions for which a learning procedure can achieve a specified generalization guarantee from data under...
Learning automaton - A learning automaton is an adaptive decision mechanism that selects actions, receives feedback from an environment and updates how it chooses future actions.
Learning rate - Learning rate is the step-size dial of iterative training: how large an adjustment the model makes to its parameters at each update, trading speed of...
Learning to rank - Learning to rank is the use of machine learning to order items according to relevance, preference or another defined criterion.
Learning vector quantization - Learning vector quantization, or LVQ, is a family of supervised classification methods that represents classes with labeled prototype vectors.
Life-time of correlation - In the source-specific stock-market study, life-time of correlation is the duration for which a rolling-window correlation coefficient remains continuously...
Lifelong Planning A* - Lifelong Planning A*, usually written LPA*, is an incremental shortest-path search algorithm that reuses information from earlier searches when a graph's...
Limited Memory AI - Limited memory AI is an informal category used to describe AI systems that use past information, learned patterns or recent observations when producing an...
Linde - Buzo - Gray algorithm - The Linde - Buzo - Gray algorithm, or LBG algorithm, is an iterative method for designing a vector quantizer.
Linear - Linear describes a relationship that follows a straight line: double the input and you double the output's contribution, with no curves, thresholds or...
Linear Dynamical System - A Linear Dynamical System is a model for a hidden state that evolves smoothly over time, with the next state a linear function of the current one plus...
Linear predictor function - A linear predictor function forms a score by adding weighted input features, usually with an intercept.
Linear separability - Linear separability is the property that two classes of points can be separated perfectly by a linear decision boundary in the chosen feature space.
Local case-control sampling - Local case-control sampling is a subsampling method for fitting logistic regression to large datasets.
Local tangent space alignment - Local tangent space alignment, or LTSA, is a nonlinear dimensionality-reduction method that estimates local tangent spaces around data points and aligns...
Log Loss - Log Loss, also called cross-entropy loss, is the standard error measure for probabilistic classifiers: it punishes wrong confident predictions severely...
Log-odds - Log-odds is the logarithm of the odds of an event: a way of stretching probability from its cramped zero-to-one scale onto an open number line where adding...
Logic learning machine - Logic Learning Machine is a rule-learning approach associated with Marco Muselli's switching neural network work and implemented in the Rulex software.
Logits - Logits are a classifier's raw output scores before they are converted into probabilities: the unbounded numbers the model actually computes, one per class...
Loss curve - A loss curve is the plot of a model's error score over the course of training: the falling, or sometimes worryingly not-falling, line that shows whether and...
Loss surface - A loss surface is the landscape of a model's error over all possible parameter settings: the hills and valleys training must descend, whose shape decides...
Lost-in-the-middle effect - The lost-in-the-middle effect is the tendency of language models to use information at the beginning and end of a long input well, but to miss or garble...
M-separation - M-separation is a graphical criterion for reading conditional-independence implications from mixed graphs, including ancestral graphs.
Machine learning - Machine learning is the study and practice of creating systems that learn useful patterns from data or experience to perform a task.
Machine learning as a service - Machine learning as a service is the delivery of ML capability over the internet as a metered product: instead of building models in-house, you call a...
Machine learning control - Machine learning control is the use of machine-learning methods to develop or improve control laws that determine a system's actions from observations.
Machine Learning Pipeline - A machine learning pipeline is the ordered chain of steps that turns raw data into predictions: collection, cleaning, features, training, evaluation and...
Machine learning system - A machine learning system is the whole working arrangement around a trained model: data, pipeline, model, serving, monitoring and the people and processes...
Machine perception - Machine perception is the use of computational methods to interpret information from sensors, such as images, sound, depth or touch.
Majority class - The majority class is the most frequent outcome in a classification dataset: the 'normal' category that most examples belong to, such as the 97 per cent of...
Manifold alignment - Manifold alignment is a family of methods for relating datasets by aligning their lower-dimensional geometric structure.
Manifold hypothesis - The manifold hypothesis is the idea that many high-dimensional datasets are concentrated near a lower-dimensional structure within the full space of...
Manifold regularization - Manifold regularization is a learning framework that encourages a prediction function to vary smoothly along the geometric structure inferred from data.
Margin classifier - A margin classifier is a classifier whose predictions or training objective use a margin separating the correct class from a decision boundary or competing...
Margin-infused relaxed algorithm - The margin-infused relaxed algorithm, or MIRA, is an online learning algorithm introduced by Crammer and Singer for classification.
Markov decision process - A Markov decision process is the standard mathematical frame for sequential decisions under uncertainty: states, actions, transition probabilities and...
Markov property - The Markov property is the assumption that the future depends only on the present state, not on the path taken to reach it: once you know where things...
Markov random field - A Markov random field, or MRF, is an undirected probabilistic graphical model that represents conditional-dependence structure among variables.