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136 relations: A priori and a posteriori, Adaptive resonance theory, Artificial intelligence, Artificial neural network, Automatic number-plate recognition, Bayes error rate, Bayes' theorem, Bayesian inference, Bayesian network, Bayesian statistics, Beta distribution, Black box, Boosting (machine learning), Bootstrap aggregating, Branch and bound, Cache language model, Categorical variable, Cluster analysis, Committee machine, Community (ecology), Compound term processing, Computer vision, Computer-aided diagnosis, Conditional random field, Conference on Computer Vision and Pattern Recognition, Conjugate prior, Contextual image classification, Correctness (computer science), Correlation clustering, Covariance matrix, Data, Data mining, Decision list, Decision theory, Decision tree, Deep learning, Dirichlet distribution, Discriminative model, Distance, Document classification, Dot product, Dynamic time warping, Engineering, Ensemble averaging (machine learning), Ensemble learning, Expected value, Face detection, Feature (machine learning), Feature extraction, Feature selection, ..., Frequentist inference, Gene expression programming, Generative model, Handwriting recognition, Hidden Markov model, Hierarchical clustering, Image analysis, Independent component analysis, Integer, Integral, K-means clustering, K-nearest neighbors algorithm, Kalman filter, Kernel principal component analysis, Kriging, Level of measurement, Linear discriminant analysis, Linear regression, List of datasets for machine learning research, List of numerical analysis software, List of numerical libraries, Logistic regression, Loss function, Machine learning, Markov random field, Maximum a posteriori estimation, Maximum likelihood estimation, Maximum-entropy Markov model, Metaheuristic, Mixture model, Mixture of experts, Multilinear principal component analysis, Multilinear subspace learning, Multinomial logistic regression, Naive Bayes classifier, Neocognitron, Neural network, Normal distribution, Occam's razor, Optimization problem, Ordinal data, Parse tree, Parsing, Part of speech, Part-of-speech tagging, Particle filter, Pattern matching, Pattern Recognition (journal), Pattern recognition (psychology), Perception, Perceptron, Perceptual learning, Peter E. Hart, Posterior probability, Power set, Predictive analytics, Principal component analysis, Prior knowledge for pattern recognition, Prior probability, Probability, Probability distribution, Probability theory, Quadratic classifier, Real number, Recurrent neural network, Regression analysis, Regular expression, Regularization (mathematics), Semi-supervised learning, Sequence labeling, Sequential pattern mining, Similarity measure, Space (mathematics), Speech recognition, Statistical classification, Statistical inference, Supervised learning, Support vector machine, Syntax, Template matching, Tensor, Text editor, Training, test, and validation sets, Unsupervised learning, Vector space, Word processor. Expand index (86 more) »
A priori and a posteriori
The Latin phrases a priori ("from the earlier") and a posteriori ("from the latter") are philosophical terms of art popularized by Immanuel Kant's Critique of Pure Reason (first published in 1781, second edition in 1787), one of the most influential works in the history of philosophy.
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Adaptive resonance theory
Adaptive resonance theory (ART) is a theory developed by Stephen Grossberg and Gail Carpenter on aspects of how the brain processes information.
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Artificial intelligence
Artificial intelligence (AI, also machine intelligence, MI) is intelligence demonstrated by machines, in contrast to the natural intelligence (NI) displayed by humans and other animals.
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Artificial neural network
Artificial neural networks (ANNs) or connectionist systems are computing systems vaguely inspired by the biological neural networks that constitute animal brains.
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Automatic number-plate recognition
Automatic number-plate recognition (ANPR; see also other names below) is a technology that uses optical character recognition on images to read vehicle registration plates to create vehicle location data.
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Bayes error rate
In statistical classification, Bayes error rate is the lowest possible error rate for any classifier of a random outcome (into, for example, one of two categories) and is analogous to the irreducible error.
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Bayes' theorem
In probability theory and statistics, Bayes’ theorem (alternatively Bayes’ law or Bayes' rule, also written as Bayes’s theorem) describes the probability of an event, based on prior knowledge of conditions that might be related to the event.
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Bayesian inference
Bayesian inference is a method of statistical inference in which Bayes' theorem is used to update the probability for a hypothesis as more evidence or information becomes available.
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Bayesian network
A Bayesian network, Bayes network, belief network, Bayes(ian) model or probabilistic directed acyclic graphical model is a probabilistic graphical model (a type of statistical model) that represents a set of variables and their conditional dependencies via a directed acyclic graph (DAG).
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Bayesian statistics
Bayesian statistics, named for Thomas Bayes (1701–1761), is a theory in the field of statistics in which the evidence about the true state of the world is expressed in terms of degrees of belief known as Bayesian probabilities.
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Beta distribution
In probability theory and statistics, the beta distribution is a family of continuous probability distributions defined on the interval parametrized by two positive shape parameters, denoted by α and β, that appear as exponents of the random variable and control the shape of the distribution.
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Black box
In science, computing, and engineering, a black box is a device, system or object which can be viewed in terms of its inputs and outputs (or transfer characteristics), without any knowledge of its internal workings.
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Boosting (machine learning)
Boosting is a machine learning ensemble meta-algorithm for primarily reducing bias, and also variance in supervised learning, and a family of machine learning algorithms that convert weak learners to strong ones.
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Bootstrap aggregating
Bootstrap aggregating, also called bagging, is a machine learning ensemble meta-algorithm designed to improve the stability and accuracy of machine learning algorithms used in statistical classification and regression.
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Branch and bound
Branch and bound (BB, B&B, or BnB) is an algorithm design paradigm for discrete and combinatorial optimization problems, as well as mathematical optimization.
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Cache language model
A cache language model is a type of statistical language model.
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Categorical variable
In statistics, a categorical variable is a variable that can take on one of a limited, and usually fixed number of possible values, assigning each individual or other unit of observation to a particular group or nominal category on the basis of some qualitative property.
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Cluster analysis
Cluster analysis or clustering is the task of grouping a set of objects in such a way that objects in the same group (called a cluster) are more similar (in some sense) to each other than to those in other groups (clusters).
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Committee machine
A committee machine is a type of artificial neural network using a divide and conquer strategy in which the responses of multiple neural networks (experts) are combined into a single response.
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Community (ecology)
In ecology, a community is a group or association of populations of two or more different species occupying the same geographical area and in a particular time, also known as a biocoenosis The term community has a variety of uses.
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Compound term processing
Compound term processing refers to a category of techniques used in information retrieval applications to perform matching on the basis of compound terms.
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Computer vision
Computer vision is a field that deals with how computers can be made for gaining high-level understanding from digital images or videos.
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Computer-aided diagnosis
Computer-aided detection (CADe), also called computer-aided diagnosis (CADx), are systems that assist doctors in the interpretation of medical images.
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Conditional random field
Conditional random fields (CRFs) are a class of statistical modeling method often applied in pattern recognition and machine learning and used for structured prediction.
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Conference on Computer Vision and Pattern Recognition
The Conference on Computer Vision and Pattern Recognition is an annual conference on computer vision and pattern recognition, by several measures regarded as the top conference in computer vision.
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Conjugate prior
In Bayesian probability theory, if the posterior distributions p(θ|x) are in the same probability distribution family as the prior probability distribution p(θ), the prior and posterior are then called conjugate distributions, and the prior is called a conjugate prior for the likelihood function.
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Contextual image classification
Contextual image classification, a topic of pattern recognition in computer vision, is an approach of classification based on contextual information in images.
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Correctness (computer science)
In theoretical computer science, correctness of an algorithm is asserted when it is said that the algorithm is correct with respect to a specification.
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Correlation clustering
Clustering is the problem of partitioning data points into groups based on their similarity.
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Covariance matrix
In probability theory and statistics, a covariance matrix (also known as dispersion matrix or variance–covariance matrix) is a matrix whose element in the i, j position is the covariance between the i-th and j-th elements of a random vector.
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Data
Data is a set of values of qualitative or quantitative variables.
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Data mining
Data mining is the process of discovering patterns in large data sets involving methods at the intersection of machine learning, statistics, and database systems.
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Decision list
Decision lists are a representation for Boolean functions which can be easily learnable from examples.
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Decision theory
Decision theory (or the theory of choice) is the study of the reasoning underlying an agent's choices.
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Decision tree
A decision tree is a decision support tool that uses a tree-like graph or model of decisions and their possible consequences, including chance event outcomes, resource costs, and utility.
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Deep learning
Deep learning (also known as deep structured learning or hierarchical learning) is part of a broader family of machine learning methods based on learning data representations, as opposed to task-specific algorithms.
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Dirichlet distribution
In probability and statistics, the Dirichlet distribution (after Peter Gustav Lejeune Dirichlet), often denoted \operatorname(\boldsymbol\alpha), is a family of continuous multivariate probability distributions parameterized by a vector \boldsymbol\alpha of positive reals.
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Discriminative model
Discriminative models, also called conditional models, are a class of models used in machine learning for modeling the dependence of unobserved (target) variables y on observed variables x. Within a probabilistic framework, this is done by modeling the conditional probability distribution P(y|x), which can be used for predicting y from x. Discriminative models, as opposed to generative models, do not allow one to generate samples from the joint distribution of observed and target variables.
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Distance
Distance is a numerical measurement of how far apart objects are.
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Document classification
Document classification or document categorization is a problem in library science, information science and computer science.
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Dot product
In mathematics, the dot product or scalar productThe term scalar product is often also used more generally to mean a symmetric bilinear form, for example for a pseudo-Euclidean space.
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Dynamic time warping
In time series analysis, dynamic time warping (DTW) is one of the algorithms for measuring similarity between two temporal sequences, which may vary in speed.
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Engineering
Engineering is the creative application of science, mathematical methods, and empirical evidence to the innovation, design, construction, operation and maintenance of structures, machines, materials, devices, systems, processes, and organizations.
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Ensemble averaging (machine learning)
In machine learning, particularly in the creation of artificial neural networks, ensemble averaging is the process of creating multiple models and combining them to produce a desired output, as opposed to creating just one model.
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Ensemble learning
In statistics and machine learning, ensemble methods use multiple learning algorithms to obtain better predictive performance that could be obtained from any of the constituent learning algorithms alone.
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Expected value
In probability theory, the expected value of a random variable, intuitively, is the long-run average value of repetitions of the experiment it represents.
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Face detection
Face detection is a computer technology being used in a variety of applications that identifies human faces in digital images.
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Feature (machine learning)
In machine learning and pattern recognition, a feature is an individual measurable property or characteristic of a phenomenon being observed.
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Feature extraction
In machine learning, pattern recognition and in image processing, feature extraction starts from an initial set of measured data and builds derived values (features) intended to be informative and non-redundant, facilitating the subsequent learning and generalization steps, and in some cases leading to better human interpretations.
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Feature selection
In machine learning and statistics, feature selection, also known as variable selection, attribute selection or variable subset selection, is the process of selecting a subset of relevant features (variables, predictors) for use in model construction.
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Frequentist inference
Frequentist inference is a type of statistical inference that draws conclusions from sample data by emphasizing the frequency or proportion of the data.
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Gene expression programming
In computer programming, gene expression programming (GEP) is an evolutionary algorithm that creates computer programs or models.
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Generative model
In statistical classification, including machine learning, two main approaches are called the generative approach and the discriminative approach.
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Handwriting recognition
Handwriting recognition (HWR) is the ability of a computer to receive and interpret intelligible handwritten input from sources such as paper documents, photographs, touch-screens and other devices.
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Hidden Markov model
Hidden Markov Model (HMM) is a statistical Markov model in which the system being modeled is assumed to be a Markov process with unobserved (i.e. hidden) states.
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Hierarchical clustering
In data mining and statistics, hierarchical clustering (also called hierarchical cluster analysis or HCA) is a method of cluster analysis which seeks to build a hierarchy of clusters.
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Image analysis
Image analysis is the extraction of meaningful information from images; mainly from digital images by means of digital image processing techniques.
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Independent component analysis
In signal processing, independent component analysis (ICA) is a computational method for separating a multivariate signal into additive subcomponents.
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Integer
An integer (from the Latin ''integer'' meaning "whole")Integer 's first literal meaning in Latin is "untouched", from in ("not") plus tangere ("to touch").
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Integral
In mathematics, an integral assigns numbers to functions in a way that can describe displacement, area, volume, and other concepts that arise by combining infinitesimal data.
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K-means clustering
k-means clustering is a method of vector quantization, originally from signal processing, that is popular for cluster analysis in data mining.
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K-nearest neighbors algorithm
In pattern recognition, the k-nearest neighbors algorithm (k-NN) is a non-parametric method used for classification and regression.
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Kalman filter
Kalman filtering, also known as linear quadratic estimation (LQE), is an algorithm that uses a series of measurements observed over time, containing statistical noise and other inaccuracies, and produces estimates of unknown variables that tend to be more accurate than those based on a single measurement alone, by estimating a joint probability distribution over the variables for each timeframe.
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Kernel principal component analysis
In the field of multivariate statistics, kernel principal component analysis (kernel PCA) is an extension of principal component analysis (PCA) using techniques of kernel methods.
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Kriging
In statistics, originally in geostatistics, kriging or Gaussian process regression is a method of interpolation for which the interpolated values are modeled by a Gaussian process governed by prior covariances, as opposed to a piecewise-polynomial spline chosen to optimize smoothness of the fitted values.
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Level of measurement
Level of measurement or scale of measure is a classification that describes the nature of information within the values assigned to variables.
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Linear discriminant analysis
Linear discriminant analysis (LDA), normal discriminant analysis (NDA), or discriminant function analysis is a generalization of Fisher's linear discriminant, a method used in statistics, pattern recognition and machine learning to find a linear combination of features that characterizes or separates two or more classes of objects or events.
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Linear regression
In statistics, linear regression is a linear approach to modelling the relationship between a scalar response (or dependent variable) and one or more explanatory variables (or independent variables).
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List of datasets for machine learning research
These datasets are used for machine-learning research and have been cited in peer-reviewed academic journals.
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List of numerical analysis software
Listed here are end-user computer applications intended for use with numerical or data analysis.
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List of numerical libraries
This is a list of notable numerical libraries, which are libraries used in software development for performing numerical calculations.
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Logistic regression
In statistics, the logistic model (or logit model) is a statistical model that is usually taken to apply to a binary dependent variable.
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Loss function
In mathematical optimization, statistics, econometrics, decision theory, machine learning and computational neuroscience, a loss function or cost function is a function that maps an event or values of one or more variables onto a real number intuitively representing some "cost" associated with the event.
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Machine learning
Machine learning is a subset of artificial intelligence in the field of computer science that often uses statistical techniques to give computers the ability to "learn" (i.e., progressively improve performance on a specific task) with data, without being explicitly programmed.
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Markov random field
In the domain of physics and probability, a Markov random field (often abbreviated as MRF), Markov network or undirected graphical model is a set of random variables having a Markov property described by an undirected graph.
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Maximum a posteriori estimation
In Bayesian statistics, a maximum a posteriori probability (MAP) estimate is an estimate of an unknown quantity, that equals the mode of the posterior distribution.
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Maximum likelihood estimation
In statistics, maximum likelihood estimation (MLE) is a method of estimating the parameters of a statistical model, given observations.
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Maximum-entropy Markov model
In machine learning, a maximum-entropy Markov model (MEMM), or conditional Markov model (CMM), is a graphical model for sequence labeling that combines features of hidden Markov models (HMMs) and maximum entropy (MaxEnt) models.
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Metaheuristic
In computer science and mathematical optimization, a metaheuristic is a higher-level procedure or heuristic designed to find, generate, or select a heuristic (partial search algorithm) that may provide a sufficiently good solution to an optimization problem, especially with incomplete or imperfect information or limited computation capacity.
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Mixture model
In statistics, a mixture model is a probabilistic model for representing the presence of subpopulations within an overall population, without requiring that an observed data set should identify the sub-population to which an individual observation belongs.
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Mixture of experts
Mixture of experts refers to a machine learning technique where multiple experts (learners) are used to divide the problem space into homogeneous regions.
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Multilinear principal component analysis
Multilinear principal component analysis (MPCA) is a multilinear extension of principal component analysis (PCA).
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Multilinear subspace learning
Multilinear subspace learning is an approach to dimensionality reduction.
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Multinomial logistic regression
In statistics, multinomial logistic regression is a classification method that generalizes logistic regression to multiclass problems, i.e. with more than two possible discrete outcomes.
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Naive Bayes classifier
In machine learning, naive Bayes classifiers are a family of simple "probabilistic classifiers" based on applying Bayes' theorem with strong (naive) independence assumptions between the features.
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Neocognitron
The neocognitron is a hierarchical, multilayered artificial neural network proposed by Kunihiko Fukushima in the 1980s.
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Neural network
The term neural network was traditionally used to refer to a network or circuit of neurons.
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Normal distribution
In probability theory, the normal (or Gaussian or Gauss or Laplace–Gauss) distribution is a very common continuous probability distribution.
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Occam's razor
Occam's razor (also Ockham's razor or Ocham's razor; Latin: lex parsimoniae "law of parsimony") is the problem-solving principle that, the simplest explanation tends to be the right one.
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Optimization problem
In mathematics and computer science, an optimization problem is the problem of finding the best solution from all feasible solutions.
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Ordinal data
Ordinal data is a categorical, statistical data type where the variables have natural, ordered categories and the distances between the categories is not known.
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Parse tree
A parse tree or parsing tree or derivation tree or concrete syntax tree is an ordered, rooted tree that represents the syntactic structure of a string according to some context-free grammar.
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Parsing
Parsing, syntax analysis or syntactic analysis is the process of analysing a string of symbols, either in natural language, computer languages or data structures, conforming to the rules of a formal grammar.
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Part of speech
In traditional grammar, a part of speech (abbreviated form: PoS or POS) is a category of words (or, more generally, of lexical items) which have similar grammatical properties.
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Part-of-speech tagging
In corpus linguistics, part-of-speech tagging (POS tagging or PoS tagging or POST), also called grammatical tagging or word-category disambiguation, is the process of marking up a word in a text (corpus) as corresponding to a particular part of speech, based on both its definition and its context—i.e., its relationship with adjacent and related words in a phrase, sentence, or paragraph.
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Particle filter
Particle filters or Sequential Monte Carlo (SMC) methods are a set of genetic, Monte Carlo algorithms used to solve filtering problems arising in signal processing and Bayesian statistical inference.
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Pattern matching
In computer science, pattern matching is the act of checking a given sequence of tokens for the presence of the constituents of some pattern.
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Pattern Recognition (journal)
Pattern Recognition is a single blind peer reviewed academic journal published by Elsevier Science.
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Pattern recognition (psychology)
In psychology and cognitive neuroscience, pattern recognition describes a cognitive process that matches information from a stimulus with information retrieved from memory.
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Perception
Perception (from the Latin perceptio) is the organization, identification, and interpretation of sensory information in order to represent and understand the presented information, or the environment.
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Perceptron
In machine learning, the perceptron is an algorithm for supervised learning of binary classifiers (functions that can decide whether an input, represented by a vector of numbers, belongs to some specific class or not).
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Perceptual learning
Perceptual learning is learning better perception skills such as differentiating two musical tones from one another or categorizations of spatial and temporal patterns relevant to real-world expertise as in reading, seeing relations among chess pieces, knowing whether or not an X-ray image shows a tumor.
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Peter E. Hart
Peter E. Hart (born c. 1940s) is an American computer scientist and entrepreneur.
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Posterior probability
In Bayesian statistics, the posterior probability of a random event or an uncertain proposition is the conditional probability that is assigned after the relevant evidence or background is taken into account.
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Power set
In mathematics, the power set (or powerset) of any set is the set of all subsets of, including the empty set and itself, variously denoted as, 𝒫(), ℘() (using the "Weierstrass p"),,, or, identifying the powerset of with the set of all functions from to a given set of two elements,.
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Predictive analytics
Predictive analytics encompasses a variety of statistical techniques from predictive modelling, machine learning, and data mining that analyze current and historical facts to make predictions about future or otherwise unknown events.
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Principal component analysis
Principal component analysis (PCA) is a statistical procedure that uses an orthogonal transformation to convert a set of observations of possibly correlated variables into a set of values of linearly uncorrelated variables called principal components.
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Prior knowledge for pattern recognition
Pattern recognition is a very active field of research intimately bound to machine learning.
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Prior probability
In Bayesian statistical inference, a prior probability distribution, often simply called the prior, of an uncertain quantity is the probability distribution that would express one's beliefs about this quantity before some evidence is taken into account.
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Probability
Probability is the measure of the likelihood that an event will occur.
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Probability distribution
In probability theory and statistics, a probability distribution is a mathematical function that provides the probabilities of occurrence of different possible outcomes in an experiment.
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Probability theory
Probability theory is the branch of mathematics concerned with probability.
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Quadratic classifier
A quadratic classifier is used in machine learning and statistical classification to separate measurements of two or more classes of objects or events by a quadric surface.
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Real number
In mathematics, a real number is a value of a continuous quantity that can represent a distance along a line.
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Recurrent neural network
A recurrent neural network (RNN) is a class of artificial neural network where connections between nodes form a directed graph along a sequence.
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Regression analysis
In statistical modeling, regression analysis is a set of statistical processes for estimating the relationships among variables.
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Regular expression
A regular expression, regex or regexp (sometimes called a rational expression) is, in theoretical computer science and formal language theory, a sequence of characters that define a search pattern.
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Regularization (mathematics)
In mathematics, statistics, and computer science, particularly in the fields of machine learning and inverse problems, regularization is a process of introducing additional information in order to solve an ill-posed problem or to prevent overfitting.
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Semi-supervised learning
Semi-supervised learning is a class of supervised learning tasks and techniques that also make use of unlabeled data for training – typically a small amount of labeled data with a large amount of unlabeled data.
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Sequence labeling
In machine learning, sequence labeling is a type of pattern recognition task that involves the algorithmic assignment of a categorical label to each member of a sequence of observed values.
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Sequential pattern mining
Sequential pattern mining is a topic of data mining concerned with finding statistically relevant patterns between data examples where the values are delivered in a sequence.
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Similarity measure
In statistics and related fields, a similarity measure or similarity function is a real-valued function that quantifies the similarity between two objects.
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Space (mathematics)
In mathematics, a space is a set (sometimes called a universe) with some added structure.
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Speech recognition
Speech recognition is the inter-disciplinary sub-field of computational linguistics that develops methodologies and technologies that enables the recognition and translation of spoken language into text by computers.
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Statistical classification
In machine learning and statistics, classification is the problem of identifying to which of a set of categories (sub-populations) a new observation belongs, on the basis of a training set of data containing observations (or instances) whose category membership is known.
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Statistical inference
Statistical inference is the process of using data analysis to deduce properties of an underlying probability distribution.
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Supervised learning
Supervised learning is the machine learning task of learning a function that maps an input to an output based on example input-output pairs.
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Support vector machine
In machine learning, support vector machines (SVMs, also support vector networks) are supervised learning models with associated learning algorithms that analyze data used for classification and regression analysis.
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Syntax
In linguistics, syntax is the set of rules, principles, and processes that govern the structure of sentences in a given language, usually including word order.
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Template matching
Template matching is a technique in digital image processing for finding small parts of an image which match a template image.
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Tensor
In mathematics, tensors are geometric objects that describe linear relations between geometric vectors, scalars, and other tensors.
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Text editor
A text editor is a type of computer program that edits plain text.
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Training, test, and validation sets
In machine learning, the study and construction of algorithms that can learn from and make predictions on data is a common task.
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Unsupervised learning
Unsupervised machine learning is the machine learning task of inferring a function that describes the structure of "unlabeled" data (i.e. data that has not been classified or categorized).
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Vector space
A vector space (also called a linear space) is a collection of objects called vectors, which may be added together and multiplied ("scaled") by numbers, called scalars.
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Word processor
A word processor is a computer program or device that provides for input, editing, formatting and output of text, often plus other features.
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Algorithms for pattern recognition, Machine pattern recognition, Pattern Recognition, Pattern Recognition and Learning, Pattern analysis, Pattern detection, Pattern recognition (machine learning), Pattern recognition and learning, Pattern recognition, visual, Pattern-recognition.
References
[1] https://en.wikipedia.org/wiki/Pattern_recognition
