Inductive programming - Inductive programming is the automatic construction of programs from examples, partial specifications or related observations.
Inferential theory of learning - The Inferential Theory of Learning is Ryszard Michalski's conceptual framework for describing learning as goal-directed transformation of knowledge through...
Information gain ratio - Information gain ratio is a decision-tree split criterion that divides information gain by the information associated with the split's branch proportions.
Input generator - An input generator is a component that produces the inputs fed to a system or model: from prompt templates and data samplers to adversarial example makers...
Input/Output Hidden Markov Model - An Input/Output Hidden Markov Model is a hidden Markov model extended so the hidden dynamics are driven by an input stream: the system's behavior depends...
Instance - An instance is a single example in a dataset: one row, one record, one case, the unit a model learns from at training time and scores at prediction time.
Instance selection - Instance selection is the process of choosing a subset of existing examples to retain for a learning task.
Intelligent automation - Intelligent automation combines AI capabilities with automated workflows to perform or support parts of a business process.
Intelligent control - Intelligent control is a broad area of control engineering that uses capabilities such as learning, reasoning, planning or knowledge-based decision-making...
Intelligent database - An intelligent database is a broad concept for a database system enhanced with capabilities such as reasoning, flexible querying, knowledge discovery or...
Intelligent decision support system - An intelligent decision support system combines decision-support tools with AI methods to help people analyze a situation, compare options and make a decision.
Intelligent word recognition - Intelligent word recognition is a term for recognizing words in document images, particularly handwritten words, using information at the word level rather...
Interactive evolutionary computation - Interactive evolutionary computation is an optimization approach in which people evaluate candidate solutions and an evolutionary algorithm uses that...
Intersection over union - Intersection over union, often written IoU, is a measure of how much two regions overlap: the shared area divided by the total area they cover together...
Inverse-gamma distribution - The inverse-gamma distribution is a continuous probability distribution over positive numbers.
Inverse-Wishart distribution - The inverse-Wishart distribution is a probability distribution over real symmetric positive-definite matrices.
Inverted Dirichlet distribution - The inverted Dirichlet distribution is a multivariate probability distribution over positive quantities.
Isotropic position - Isotropic position describes a normalization in which a vector distribution has equal second-moment spread in every direction.
Iteration - An iteration is one complete round of an improvement process: one training update, one tuning cycle or one review pass, after which the result is measured...
Iteratively Reweighted Least Squares - Iteratively Reweighted Least Squares is a fitting method that solves certain regression problems by repeatedly solving a weighted least-squares problem...
Jackknife variance estimates for random forest - Jackknife variance estimates for random forests are methods for estimating how much a forest's prediction would vary across repeated training samples.
Jarvis&Patrick clustering - Jarvis-Patrick clustering is a clustering method that groups items by shared near neighbors: two items belong together when they appear high on each other's...
Jensen bound integration - Jensen bound integration is a technique for approximating an average that is hard to compute exactly: it uses Jensen's inequality, which relates the average...
Junction tree algorithm - The junction tree algorithm is a method for exact probabilistic inference in suitable graphical models.
K-line (artificial intelligence) - A K-line is a proposed memory mechanism in Marvin Minsky's theory of mind: a connection structure that can reactivate selected mental processes associated...
K-nearest Neighbor Classification Algorithm - K-nearest Neighbor Classification is a method that classifies a case by finding the k most similar labeled cases in the training data and letting them vote...
Kernel - A kernel is a function that measures the similarity of two cases, used by kernel methods to work in an enriched feature space without ever computing the...
Kernel density estimation - Kernel density estimation is a nonparametric method for estimating a probability density from observed data.
Kernel embedding of distributions - Kernel embedding of distributions represents a probability distribution as an average of feature representations in a reproducing kernel Hilbert space.
Kernel methods for vector output - Kernel methods for vector output extend kernel learning to predict several output values together.
Kernel Support Vector Machines - Kernel Support Vector Machines are SVMs equipped with a kernel function, letting them find non-linear boundaries by working implicitly in an enriched...
Keypoints - Keypoints are specific, meaningful locations detected on an object or image, such as the corners of a document, the joints of a body or the landmarks of a...
Knowledge-based configuration - Knowledge-based configuration is the use of explicit domain knowledge to assemble a product, service or system from available components while satisfying...
Knowledge-based recommender system - A knowledge-based recommender system suggests items by reasoning about explicit user requirements and knowledge of how items meet those requirements.
Knowledge-based systems - Knowledge-based systems are computer systems that use explicitly represented knowledge to support reasoning, problem solving or decisions.
Knowledge compilation - Knowledge compilation is the transformation of a knowledge representation into another form that supports selected reasoning queries more efficiently.
Knowledge integration - Knowledge integration is the process of combining knowledge from different sources into a form that can be used together coherently.
Kolmogorov - Arnold Networks - Kolmogorov - Arnold networks, usually called KANs, are neural-network architectures that learn univariate functions on connections between nodes.
L 1 regularization - L1 regularization is a penalty added to training that charges the model for the size of its weights, with the distinctive effect of pushing unneeded weights...
L 2 regularization - L2 regularization is a penalty added to training that charges the model for the square of its weights, shrinking all weights toward zero to keep any single...
L1-based feature selection - L1-based feature selection uses a model penalized by the sum of absolute coefficient values to identify a smaller set of input features.
Label - A label is the known answer attached to an example: the category, number or outcome the model is supposed to learn to predict, such as spam, churned or 42000.
Label leakage - Label leakage is the accidental inclusion, in a model's inputs, of information that is derived from or equivalent to the answer, so the model learns to read...
Label noise - Label noise is error or unreliability in the target labels used to train or evaluate a supervised learning system.
Label Propagation - Label Propagation is a semi-supervised method that spreads labels from the few cases that have them through a similarity network, so unlabeled cases inherit...
Labeled example - A labeled example is a single training case with its known answer attached: the inputs plus the label, the atomic unit of supervised learning.
Lagged features - Lagged features are inputs built from the past values of a series: yesterday's sales, last week's balance, the same customer a month ago, giving a model...
Lambda - Lambda is the traditional symbol for the regularization strength dial: the number that decides how heavily a model is penalized for large weights, trading...