Pattern recognition is a field within the area of machine learning. Alternatively, it can be defined as As a broad subfield of artificial intelligence, Machine learning is concerned with the development of algorithms and techniques, which allow computers to learn. At a general level, there are two types of learning: inductive, and deductive. ...

"the act of taking in raw data and taking an action based on the category of the data" [1].

As such, it is a collection of methods for supervised learning. Look up category in Wiktionary, the free dictionary. ... Supervised learning is a machine learning technique for creating a function from training data. ...

Pattern recognition aims to classify data (patterns) based on either a priori knowledge or on statistical information extracted from the patterns. The patterns to be classified are usually groups of measurements or observations, defining points in an appropriate multidimensional space. Data is the plural of datum. ... A pattern is a form, template, or model (or, more abstractly, a set of rules) which can be used to make or to generate things or parts of a thing, especially if the things that are generated have enough in common for the underlying pattern to be inferred or discerned... A priori is a Latin phrase meaning from the former or less literally before experience. In much of the modern Western tradition, the term a priori is considered to mean propositional knowledge that can be had without, or prior to, experience. ... A graph of a bell curve in a normal distribution showing statistics used in educational assessment, comparing various grading methods. ... Attempting to understand the nature of space has always been a prime occupation for philosophers and scientists. ...

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 classification or description scheme that does the actual job of classifying or describing observations, relying on the extracted features. To meet Wikipedias quality standards, this article or section may require cleanup. ... Feature extraction is an area of image processing which involves using algorithms to detect and isolate various desired portions of a digitized image or video stream. ... Statistical classification is a type of supervised learning problem in which labeled training data is used to create a function that will correctly predict the label of future data. ...

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 characterised as supervised learning. Learning can also be unsupervised, in the sense that the system is not given an a priori labelling of patterns, instead it establishes the classes itself based on the statistical regularities of the patterns. A training set is used in artificial intelligence, together with a supervised training method, and it consists of an input vector and an answer vector. ... Supervised learning is a machine learning technique for creating a function from training data. ... Unsupervised learning is a method of machine learning where a model is fit to observations. ...

The classification or description scheme usually uses one of the following approaches: statistical (or decision theoretic), syntactic (or structural). Statistical pattern recognition is based on statistical characterisations of patterns, assuming that the patterns are generated by a probabilistic system. 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 Bayesian classifiers to much more powerful neural networks. Statistical classification is a type of supervised learning problem in which labeled training data is used to create a function that will correctly predict the label of future data. ... The word probability derives from the Latin probare (to prove, or to test). ... A naive Bayes classifier (also known as Idiots Bayes) is a simple probabilistic classifier. ... An artificial neural network (ANN), also called a simulated neural network (SNN) or commonly just neural network (NN) is an interconnected group of artificial neurons that uses a mathematical or computational model for information processing based on a connectionist approach to computation. ...

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. Holographic Associative Memory is part of the family of analog, correlation-based, associative, stimulus-response memories, where information is mapped onto the phase orientation of complex numbers operating. ...

Typical applications are automatic speech recognition, classification of text into several categories (e.g. spam/non-spam email messages), the automatic recognition of handwritten postal codes on postal envelopes, or the 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. Speech recognition technologies allow computers equipped with a source of sound input, such as a microphone, to interpret human speech, for example, for transcription or as an alternative method of interacting with a computer. ... Document classification is a problem in information science. ... It has been suggested that on-line handwriting recognition be merged into this article or section. ... A facial recognition system is a computer-driven application for automatically identifying a person from a digital image. ... Image analysis is the extraction of useful information from images; mainly from digital images by means of digital image processing techniques. ...

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. Psychology (Gk: psyche, soul or mind + logos, speech) is an academic and applied field involving the study of the mind, brain, and behavior, both human and nonhuman. ... Ethology is the scientific study of animal behavior considered as a branch of zoology. ...   Computer science is the study of the theoretical foundations of information and computation and their implementation and application in computer systems. ...

External links

• List of Pattern Recognition web sites
• Journal of Pattern Recognition Research

• Pattern Recognition Using Neural Networks : A Sample Project in Microsoft.NET

References

• Richard O. Duda, Peter E. Hart, David G. Stork (2001) Pattern classification (2nd edition), Wiley, New York, ISBN 0471056693.

• 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 0471135348

• Sholom Weiss and Casimir Kulikowski (1991) Computer Systems That Learn, Morgan Kaufmann. ISBN 1-55860-065-5