Granular computing - Granular computing is an approach to representing and solving problems using meaningful groups or units of information called granules.
Graph semantics - Graph semantics is the meaning carried by the structure of a graph: what the nodes represent, what the edges assert and how relationships between things...
Graphical lasso - Graphical lasso is a statistical method for estimating a sparse inverse covariance matrix using an L1 penalty.
Graphical time warping - Graphical time warping is a framework for jointly aligning multiple pairs of time series while modeling relationships among their alignment paths.
Grey Wolf Optimization - Grey Wolf Optimization is a population-based numerical search method inspired by an abstract model of gray-wolf social roles and hunting.
Ground truth - Ground truth is the verified, real-world answer for a case, used as the standard against which a model's predictions are trained, tested and judged.
Groundedness - Groundedness is the degree to which an AI system's output is anchored in checkable sources or real evidence, rather than generated freely from patterns the...
Grounding - Grounding is the practice of connecting a model's output to specific sources or real-world referents: the work done, in architecture and in process, so that...
Grouped Dirichlet distribution - The grouped Dirichlet distribution is an extension of the Dirichlet family used to represent probabilities for categories when some observations identify...
Growing self-organizing map - A growing self-organizing map is an unsupervised neural-network method that expands its map of representative units as it learns the structure of data.
Guardrails - Guardrails are the constraints, checks and boundaries placed around an AI system to keep its behavior within acceptable limits: what it may say, do, spend...
Handwriting recognition - Handwriting recognition is the process of converting handwritten marks into machine-readable text or symbols.
Hannan - Quinn information criterion - The Hannan - Quinn information criterion is a score for comparing statistical models that balances goodness of fit against model complexity.
Hard clustering - Hard clustering is clustering where every item belongs to exactly one cluster, with no partial or shared membership: each customer, document or transaction...
Hashing - Hashing is the conversion of data of any size into a fixed-length code, called a hash, so that items can be stored, compared, retrieved or anonymized...
Hidden Markov Decision Tree - A Hidden Markov Decision Tree is a hybrid model that grows a decision tree over sequential data, with hidden states at the nodes, so that each branch of the...
Hidden Markov Model with Duration - A Hidden Markov Model with Duration is a sequence model that extends the classic hidden Markov model by explicitly modeling how long the system stays in...
Hidden Markov random field - A Hidden Markov random field is a model for data arranged in a grid or network, most often images, where hidden labels at each location depend on their...
Hidden Markov Segment Model - A Hidden Markov Segment Model is a sequence model that treats observations as arriving in variable-length segments, each generated as a unit by one hidden...
Hierarchical Classification - Hierarchical Classification is classification into a taxonomy of categories that sit inside one another, where a prediction can name a broad class, a...
Hierarchical clustering - Hierarchical clustering is clustering that builds a tree of groupings, from every item in its own cluster up to everything in one cluster, so you can cut...
Hierarchical control system - A hierarchical control system organizes control responsibilities into levels, with higher levels setting broader goals and lower levels handling more...
Hierarchical navigable small world - Hierarchical navigable small world, usually abbreviated HNSW, is a graph-based method for approximate nearest-neighbor search.
Highway network - A highway network is a neural-network architecture that uses learned gates to control how information is transformed or carried through layers.
Hill climbing - Hill climbing is a search method that improves a solution by repeatedly making the small change that helps most right now, stopping when no small change...
Hindsight optimization - Hindsight optimization is a planning approach that evaluates actions by sampling possible future uncertainty and solving the resulting deterministic...
Histogram-Based Gradient Boosting - Histogram-based gradient boosting builds boosted decision trees using binned feature values and aggregated statistics.
Histogram of oriented displacements - A histogram of oriented displacements is a trajectory descriptor that summarizes movement by accumulating displacement directions into bins.
Ho - Kashyap algorithm - The Ho - Kashyap algorithm is an iterative method for solving a system of linear inequalities, with an important application to learning a linear classifier.
HOG feature - A HOG feature, from Histogram of Oriented Gradients, is a way of describing a patch of an image by the directions of its edges, counting how strongly the...
Holdout data - Holdout data is the portion of available data deliberately kept away from training, reserved to test the finished model on cases it has never seen, so the...
Human - AI interaction - Human - AI interaction is the study and design of how people communicate, collaborate and make decisions with AI systems.
Human-based evolutionary computation - Human-based evolutionary computation uses people's contributions within an evolutionary search process to create, modify, combine or evaluate candidate...
Human-centered AI - Human-centered AI is an approach to designing and operating AI around people's needs, capabilities, values and responsibilities.
Hybrid intelligent system - A hybrid intelligent system combines two or more different AI or computational-intelligence approaches within one problem-solving system.
Hyper basis function network - A hyper basis function network is a generalization of a radial basis function network that allows a more flexible measure of distance around its...
Hyper-parameter - A hyper-parameter is a setting chosen before training that controls how the model learns, rather than something the model learns from the data itself: how...
Hyperplane - A hyperplane is a flat boundary that divides the space of inputs into two sides, the geometric object a linear classifier learns: points on one side get one...
I.i.d - i.i.d, short for independent and identically distributed, is the assumption that each data point was drawn from the same underlying process, unaffected by...
ID3 algorithm - ID3 is a decision-tree learning algorithm that recursively chooses attributes using information gain.
IDistance - iDistance is an indexing method that supports nearest-neighbor search by mapping multidimensional points to one-dimensional keys.
In-set condition - An in-set condition is a constraint or test that checks whether a case belongs to the set the model was built for: inside the population, ranges and...
Inauthentic text - Inauthentic text, in a specific strand of text-analysis research, means syntactically well-formed writing that lacks meaning as a whole.
Incremental heuristic search - Incremental heuristic search solves a sequence of related search problems by reusing information from earlier searches and using a heuristic to guide the work.
Independent component analysis - Independent component analysis is a statistical method for expressing observed data as mixtures of underlying components that are as statistically...
Induction - Induction is reasoning from examples to a general rule: the logical move at the heart of machine learning, where many observed cases are compressed into a...
Inductive bias - Inductive bias is the set of assumptions or preferences that helps a learning system generalize beyond its training examples.
Inductive probability - Inductive probability concerns assigning probabilities to conclusions or future observations on the basis of incomplete evidence.