---
title: "Pattern recognition"
chunk: 4/4
source: "https://en.wikipedia.org/wiki/Pattern_recognition"
category: "reference"
tags: "science, encyclopedia"
date_saved: "2026-05-05T03:56:51.565383+00:00"
instance: "kb-cron"
---

identification and authentication: e.g., license plate recognition, fingerprint analysis, face detection/verification, and voice-based authentication.
medical diagnosis: e.g., screening for cervical cancer (Papnet), breast tumors or heart sounds;
defense: various navigation and guidance systems, target recognition systems, shape recognition technology etc.
mobility: advanced driver assistance systems, autonomous vehicle technology, etc.
In psychology, pattern recognition is used to make sense of and identify objects, and is closely related to perception. This explains how the sensory inputs humans receive are made meaningful. Pattern recognition can be thought of in two different ways. The first concerns template matching and the second concerns feature detection. A template is a pattern used to produce items of the same proportions. The template-matching hypothesis suggests that incoming stimuli are compared with templates in the long-term memory. If there is a match, the stimulus is identified. Feature detection models, such as the Pandemonium system for classifying letters (Selfridge, 1959), suggest that the stimuli are broken down into their component parts for identification. One observation is a capital E having three horizontal lines and one vertical line.

== Algorithms ==
Algorithms for pattern recognition depend on the type of label output, on whether learning is supervised or unsupervised, and on whether the algorithm is statistical or non-statistical in nature. Statistical algorithms can further be categorized as generative or discriminative.

=== Classification methods (methods predicting categorical labels) ===

Parametric:

Linear discriminant analysis
Quadratic discriminant analysis
Maximum entropy classifier (aka logistic regression, multinomial logistic regression): Note that logistic regression is an algorithm for classification, despite its name. (The name comes from the fact that logistic regression uses an extension of a linear regression model to model the probability of an input being in a particular class.)
Nonparametric:

Decision trees, decision lists
Kernel estimation and K-nearest-neighbor algorithms
Naive Bayes classifier
Neural networks (multi-layer perceptrons)
Perceptrons
Support vector machines
Gene expression programming

=== Clustering methods (methods for classifying and predicting categorical labels) ===

Categorical mixture models
Hierarchical clustering (agglomerative or divisive)
K-means clustering
Correlation clustering
Kernel principal component analysis (Kernel PCA)

=== Ensemble learning algorithms (supervised meta-algorithms for combining multiple learning algorithms together) ===

Boosting (meta-algorithm)
Bootstrap aggregating ("bagging")
Ensemble averaging
Mixture of experts, hierarchical mixture of experts

=== General methods for predicting arbitrarily-structured (sets of) labels ===
Bayesian networks
Markov random fields

=== Multilinear subspace learning algorithms (predicting labels of multidimensional data using tensor representations) ===
Unsupervised:

Multilinear principal component analysis (MPCA)

=== Real-valued sequence labeling methods (predicting sequences of real-valued labels) ===

Kalman filters
Particle filters

=== Regression methods (predicting real-valued labels) ===

Gaussian process regression (kriging)
Linear regression and extensions
Independent component analysis (ICA)
Principal components analysis (PCA)

=== Sequence labeling methods (predicting sequences of categorical labels) ===
Conditional random fields (CRFs)
Hidden Markov models (HMMs)
Maximum entropy Markov models (MEMMs)
Recurrent neural networks (RNNs)
Dynamic time warping (DTW)

== See also ==
Adaptive resonance theory – Theory in neuropsychology
Black box – System where only the inputs and outputs can be viewed, and not its implementation
Cache language model
Compound-term processing
Computer-aided diagnosis – Type of diagnosis assisted by computers
Contextual image classification
Data mining – Process of analyzing large data sets
Deep learning – Branch of machine learning
Grey box model – Mathematical data production model with limited structure
Information theory – Scientific study of digital information
List of datasets for machine learning research
List of numerical-analysis software
List of numerical libraries
Neocognitron – Type of artificial neural network
Perception – Interpretation of sensory information
Perceptual learning – Process of learning better perception skills
Predictive analytics – Statistical techniques analyzing facts to make predictions about unknown events
Prior knowledge for pattern recognition
Sequence mining – Data mining techniquePages displaying short descriptions of redirect targets
Template matching – Technique in digital image processing

== References ==

== Further reading ==
Fukunaga, Keinosuke (1990). Introduction to Statistical Pattern Recognition (2nd ed.). Boston: Academic Press. ISBN 978-0-12-269851-4.
Hornegger, Joachim; Paulus, Dietrich W. R. (1999). Applied Pattern Recognition: A Practical Introduction to Image and Speech Processing in C++ (2nd ed.). San Francisco: Morgan Kaufmann Publishers. ISBN 978-3-528-15558-2.
Schuermann, Juergen (1996). Pattern Classification: A Unified View of Statistical and Neural Approaches. New York: Wiley. ISBN 978-0-471-13534-0.
Godfried T. Toussaint, ed. (1988). Computational Morphology. Amsterdam: North-Holland Publishing Company. ISBN 978-1-4832-9672-2.
Kulikowski, Casimir A.; Weiss, Sholom M. (1991). Computer Systems That Learn: Classification and Prediction Methods from Statistics, Neural Nets, Machine Learning, and Expert Systems. San Francisco: Morgan Kaufmann Publishers. ISBN 978-1-55860-065-2.
Duda, Richard O.; Hart, Peter E.; Stork, David G. (2000). Pattern Classification (2nd ed.). Wiley-Interscience. ISBN 978-0-471-05669-0.
Jain, Anil.K.; Duin, Robert.P.W.; Mao, Jianchang (2000). "Statistical pattern recognition: a review". IEEE Transactions on Pattern Analysis and Machine Intelligence. 22 (1): 4–37. Bibcode:2000ITPAM..22....4J. CiteSeerX 10.1.1.123.8151. doi:10.1109/34.824819. S2CID 192934.
An introductory tutorial to classifiers (introducing the basic terms, with numeric example)
Kovalevsky, V. A. (1980). Image Pattern Recognition. New York, NY: Springer New York. ISBN 978-1-4612-6033-2. OCLC 852790446.

== External links ==
The International Association for Pattern Recognition
List of Pattern Recognition web sites
Journal of Pattern Recognition Research Archived 2008-09-08 at the Wayback Machine
Pattern Recognition Info
Pattern Recognition (Journal of the Pattern Recognition Society)
International Journal of Pattern Recognition and Artificial Intelligence Archived 2004-12-11 at the Wayback Machine
International Journal of Applied Pattern Recognition
Open Pattern Recognition Project, intended to be an open source platform for sharing algorithms of pattern recognition
Improved Fast Pattern Matching Improved Fast Pattern Matching