Pattern recognition {{Otheruses}} '''Pattern recognition''' is a sub-topic of [[machine learning]]. It can be defined as :"the act of taking in raw data and taking an action based on the [[Category (taxonomy)|category]] of the data". Most research in pattern recognition is about methods for [[supervised learning]] and [[unsupervised learning]]. Pattern recognition aims to classify [[data]] ([[pattern]]s) based on either ''[[A priori and a posteriori (philosophy)|a priori]]'' knowledge or on [[statistics|statistical]] information extracted from the patterns. The patterns to be classified are usually groups of measurements or observations, defining points in an appropriate [[space (mathematics)|multidimensional space]]. This is in contrast to '''[[pattern matching]]''', where the pattern is rigidly specified. ==Overview== A complete pattern recognition system consists of a [[sensor]] that gathers the observations to be classified or described; a [[feature extraction]] mechanism that computes numeric or symbolic information from the observations; and a [[statistical classification|classification]] or description scheme that does the actual job of classifying or describing observations, relying on the extracted features. The classification or description scheme is usually based on the availability of a set of patterns that have already been classified or described. This set of patterns is termed the [[training set]] and the resulting learning strategy is characterized as [[supervised learning]]. Learning can also be [[unsupervised learning|unsupervised]], in the sense that the system is not given an ''a priori'' labeling of patterns, instead it establishes the classes itself based on the statistical regularities of the patterns. The classification or description scheme usually uses one of the following approaches: [[statistical classification|statistical]] (or decision theoretic), [[syntactic pattern recognition|syntactic]] (or structural). Statistical pattern recognition is based on statistical characterisations of patterns, assuming that the patterns are generated by a [[probabilistic]] system. Syntactical (or structural) pattern recognition is based on the structural interrelationships of features. A wide range of algorithms can be applied for pattern recognition, from very simple [[Naive Bayes classifier|Bayesian classifiers]] to much more powerful [[Artificial neural network|neural networks]]. An intriguing problem in pattern recognition yet to be solved is the relationship between the problem to be solved (data to be classified) and the performance of various pattern recognition algorithms (classifiers). Pattern recognition is more complex when templates are used to generate variants. For example, in English, sentences often follow the "N-VP" (noun - verb phrase) pattern, but some knowledge of the English language is required to detect the pattern. Pattern recognition is studied in many fields, including [[psychology]], [[ethology]], and [[computer science]]. [[Holographic associative memory]] is another type of pattern matching scheme where a target small patterns can be searched from a large set of learned patterns based on cognitive meta-weight. ==Uses== Within medical science pattern recognition creates the basis for [[computer-aided diagnosis]] (CAD) systems. CAD describes a procedure that supports the doctor's interpretations and findings. Typical applications are automatic [[speech recognition]], [[document classification|classification of text into several categories]] (e.g. spam/non-spam email messages), the [[handwriting recognition|automatic recognition of handwritten postal codes]] on postal envelopes, or the [[facial recognition system|automatic recognition of images]] of human faces. The last two examples form the subtopic [[image analysis]] of pattern recognition that deals with digital images as input to pattern recognition systems. ==See also== * [[Computer-aided diagnosis]] * [[List of numerical analysis software]] * [[Machine learning]] * [[Data mining]] * [[Prior knowledge for pattern recognition]] * [[Predictive analytics]] * [[EURASIP Journal on Advances in Signal Processing]] ==References== * [[Keinosuke Fukunaga]], (1990) ''Statistical Pattern Recognition'', Morgan Kaufmann, ISBN 0-12-269851-7. *[[Christopher M. Bishop]], (2006) ''Pattern Recognition and Machine Learning'', Springer, ISBN 0-387-31073-8. * [[Sergios Theodoridis]], [[Konstantinos Koutroumbas]], (2006) ''Pattern Recognition'' (3rd edition), Elsevier, ISBN 0-12-369531-7. * [[Phiroz Bhagat]], (2005) ''Pattern Recognition in Industry'' Elsevier, ISBN 0-08-044538-1. * [[Richard O. Duda]], [[Peter E. Hart]], [[David G. Stork]] (2001) ''Pattern classification'' (2nd edition), Wiley, New York, ISBN 0-471-05669-3. * Dietrich Paulus and Joachim Hornegger (1998) ''Applied Pattern Recognition'' (2nd edition), Vieweg. ISBN 3-528-15558-2 * J. Schuermann: ''Pattern Classification: A Unified View of Statistical and Neural Approaches'', Wiley&Sons, 1996, ISBN 0-471-13534-8 * Sholom Weiss and Casimir Kulikowski (1991) ''Computer Systems That Learn'', Morgan Kaufmann. ISBN 1-55860-065-5 ==External links== * [http://www.iapr.org The International Association for Pattern Recognition] * [http://cgm.cs.mcgill.ca/~godfried/teaching/pr-web.html List of Pattern Recognition web sites] * [http://www.jprr.org Journal of Pattern Recognition Research] * [http://www.docentes.unal.edu.co/morozcoa/docs/mvapr/ Multivariate Analysis and Pattern Recognition Team] or [http://www.mvapr.co.nr http://www.mvapr.co.nr] * [http://www.docentes.unal.edu.co/morozcoa/docs/mvapr/techniques.html Recommended Software for Multivariate Analysis and Pattern Recognition] or [http://www.mvapr.co.nr/techniques.html http://www.mvapr.co.nr/techniques.html] * [http://www.docentes.unal.edu.co/morozcoa/docs/mvapr/education.html Recommended Texbooks on Multivariate Analysis and Pattern Recognition] or [http://www.mvapr.co.nr/education.html http://www.mvapr.co.nr/education.html] * [http://www.sciencedirect.com/science/journal/00313203 Pattern Recognition] (Journal of the Pattern Recognition Society) * [http://www.alyuda.com/ Tools for pattern recognition, data mining and forecasting] * Neocognitron '''application''' (C#) to recognize patterns with '''how to videos''' are available: [http://neocognitron.euweb.cz/ here] * [http://en.wikipedia.org/wiki/List_of_computer_vision_conferences] List of computer vision conferences {{FOLDOC}} [[Category:Classification algorithms]] [[Category:Machine learning]] [[Category:Statistical classification]] [[Category:Formal sciences]] [[ar:تمييز الأنماط]] [[de:Mustererkennung]] [[es:Reconocimiento de patrones]] [[fr:Reconnaissance de formes]] [[ko:패턴 인식]] [[id:Pengenalan pola]] [[lt:Atpažinimo teorija]] [[ms:Pengecaman pola]] [[nl:Patroonherkenning]] [[ja:パターン認識]] [[pl:Rozpoznawanie wzorców]] [[pt:Reconhecimento de padrões]] [[ru:Распознавание образов]] [[fi:Hahmontunnistus]] [[th:การรู้จำแบบ]] [[tr:Örüntü Tanıma]] [[vi:Nhận dạng mẫu]] [[zh:模式识别]]