Centroid - A centroid is the center point of a group of data, found by averaging the group's values on each measure, and used to represent the whole group with one...
Centroid-based clustering - Centroid-based clustering groups data by assigning every point to the nearest center, or centroid, with k-means the best-known method, producing a chosen...
Characteristic samples - A characteristic sample is a set of labeled examples sufficient for a particular learning algorithm to identify a particular target language under a...
Circuit (neural network) - In neural-network interpretability, a circuit is a collection of internal components and connections that work together to produce a particular behavior.
Circular analysis - Circular analysis occurs when information used to select a pattern also enters the analysis presented as independent evidence for that pattern, without a...
Class - In classification, a class is one of the categories a model can assign to an item, such as spam, billing or delivery for incoming emails.
Class activation mapping - Class activation mapping, or CAM, is a technique that highlights image regions contributing to a particular class score in certain convolutional neural...
Classification - Classification is the task of assigning items to predefined categories, such as sorting emails into spam or not spam, using patterns learned from labeled...
Classification model - A classification model is a model that sorts items into predefined categories, such as spam or not spam, by learning the patterns that separate the classes...
Classification threshold - A classification threshold is the cut-off that turns a model's probability score into a decision, such as flagging every customer scored 0.7 or higher as a...
Classifier chains - Classifier chains are a method for multi-label classification in which several classifiers are linked in an order.
Clipping - Clipping limits values to a chosen range, replacing anything above a maximum or below a minimum with the boundary value, and is used in AI to keep numbers...
Clusterer - A clusterer is a model or algorithm that groups items by similarity, so that items in the same group are more alike each other than items in other groups...
Clustering - Clustering is the task of grouping items by similarity so that items within a group are more alike than items in different groups, discovered from the data...
Clustering assumption - The clustering assumption is the idea, used in semi-supervised learning, that data points falling in the same natural cluster probably share the same label...
Clustering error - Clustering error is a measure of how wrong a clustering's group assignments are, usually counted as the smallest number of items that would need to move...
Clustering high-dimensional data - Clustering high-dimensional data means grouping records described by many features, often hundreds or thousands.
Clustering illusion - The clustering illusion is the tendency to see meaningful patterns or causes in clusters and streaks that can arise through chance.
CN2 algorithm - The CN2 algorithm is a rule-induction method that learns classification rules from labeled examples.
Co-adaptation - Co-adaptation is a failure mode in neural networks where individual units become useful only in the company of specific other units, making the network...
Co-training - Co-training is a semi-supervised method where two models learn the same task from two different views of the data, each labeling examples it is confident...
Cognitive robotics - Cognitive robotics is the study and engineering of robots that connect perception, learning, reasoning and action to operate adaptively in their surroundings.
Coherent extrapolated volition - Coherent extrapolated volition, or CEV, is a proposal in AI-alignment thinking that an advanced AI should be guided by what people would collectively want...
Complex inverse Wishart distribution - The complex inverse Wishart distribution is a probability distribution over complex-valued Hermitian positive-definite matrices.
Complex Wishart distribution - The complex Wishart distribution describes random matrices formed from sums of outer products of suitable complex Gaussian observation vectors.
Computational intelligence - Computational intelligence is a broad field of methods for building adaptive computational systems, especially neural networks, fuzzy systems and...
Computer audition - Computer audition, also called machine listening, is the use of computational methods to interpret sound.
Concept drift - Concept drift is the gradual or sudden change in the relationship between a model's inputs and its target, so that patterns the model learned stop matching...
Conceptual clustering - Conceptual clustering groups examples while also developing descriptions of the resulting groups.
Configuration - Configuration is the set of choices and settings that control how an AI system runs, from model options and thresholds to file paths, limits and...
Confusion matrix - A confusion matrix is a table that lays a classifier's predictions against the true classes, showing not just how often it is right but exactly which...
Connectionist expert system - A connectionist expert system uses a neural-style network to represent and apply knowledge for a specialized task.
Connectionist temporal classification - Connectionist temporal classification, or CTC, is a training method for sequence models that learn from unaligned data, where the input and output sequences...
Consensus clustering - Consensus clustering combines information from several clustering results to identify patterns of agreement.
Consistency regularization - Consistency regularization is a training principle for semi-supervised learning: a model's prediction for an example should stay the same when that example...
Constituency parsing - Constituency parsing is the analysis of a sentence's grammatical structure as a tree of nested phrases, such as noun phrases and verb phrases, showing how...
Constrained clustering - Constrained clustering forms groups while taking additional requirements or background knowledge into account.
Constrained Hidden Markov Model - A constrained hidden Markov model is a hidden Markov model with extra rules limiting which states or transitions are allowed, so the model can only produce...
Constrained mixture model - A constrained mixture model is a mixture model whose components are tied by extra rules, such as shared shapes or linked centers, so the mixture fits...
Constructing skill trees - Constructing Skill Trees, or CST, is a hierarchical reinforcement-learning method that builds reusable skills from demonstrated solution trajectories.
Constructive cooperative coevolution - Constructive cooperative coevolution, often written C3, is an optimization approach that builds feasible starting solutions and then improves their...
Continuous feature - A continuous feature is an input variable that can take any numeric value within a range, such as height, temperature, income or delivery time, rather than...
Convergence - Convergence is the point in training when a model stops improving meaningfully, when further rounds of adjustment change so little that continuing adds...
Convex clustering - Convex clustering is a clustering method that poses the grouping task as a single smooth optimization problem with one best answer, avoiding the...
Cooperative coevolution - Cooperative coevolution is an evolutionary optimization approach in which different populations evolve parts of a solution and are evaluated through their...
Correlation clustering - Correlation clustering groups items using pairwise judgments of similarity and difference, seeking a partition that agrees with those judgments as much as...