Pattern recognition algorithms generally aim to provide a reasonable answer for all possible inputs and to do "fuzzy" matching of inputs.
Pattern Recognition
Pattern recognition may seem obvious or trivial, but it is an essential, basic component of the way people learn.
Pattern Recognition Information including books, a list of review papers, and bibliographic search.
The AUAI Tutorials
David W. Aha's Machine Learning Resources and Tutorials
"Neural Networks" by Jordan and Bishop.
Driver's affect We have begun an effort to outfit a car and driver with various sensors and pattern recognition that aims to recognize important affective states of the driver such as anger or stress. Experimental results are forthcoming.
Pattern Recognition
Pattern recognition involves determining the characteristics in specific samples and sorting them into classes; a process called classification.
pattern recognition When a program makes observations of some kind, it is often programmed to compare what it sees with a pattern. For example, a vision program may try to match a pattern of eyes and a nose in a scene in order to find a face.
Pattern Recognition
Finding and Recognizing Patterns in Data
AITopics > Machine Learning > Pattern Recognition ...
Pattern recognition
To distinguish pedestrians from dogs and cars on captured image sequence of traffic data is a classification problem.
Learning ...
Pattern Recognition
Pattern recognition [38] aims to classify data (patterns) based on either a priori knowledge or on statistical information extracted from the patterns.
Pattern Recognition
The use of feature analysis to identify an image of an object. May involve techniques such as statistical pattern recognition, Bayesian analysis, classification, cluster analysis, and analysis of texture and edges.
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
Natural language understanding and machine translation
Winograd's SHRDLU (early 1970's) ...
Pattern recognition is a field within the area of machine learning. ...
The pattern recognition neural net architecture would have been present prior to the development of written language.
ADALINE, Pattern Recognition, Classification of Digits 0-9
BPN, Time-Series Forecasting, Prediction of the Annual Number of Sunspots
HOPFIELD, Autoassociative Memory, Associative Recall of Images ...
Classification/Pattern recognition:
The task of pattern recognition is to assign an input pattern (like handwritten symbol) to one of many classes. This category includes algorithmic implementations such as associative memory.
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It worked by simple pattern recognition and substitution of key words into canned phrases. It was so convincing, however, that there are many anecdotes about people becoming very emotionally caught up in dealing with ELIZA.
Selfridge, "Pattern recognition and modern computers,"
[13] G. P. Dinneen, "Programming pattern recognition," in [A].
[14] M. L. Minsky, "Heuristic Aspects of the Artificial Intelligence Problem," Lincoln Lab., M.I.T., Lexington, Mass., Group Rept.
Chris Bishop's book Neural Networks for Pattern Recognition, Oxford University Press is a good start on this area.
Pattern recognition: Ability to recognize a given sub pattern within a much larger pattern. Alternatively, a machine capable of pattern recognition can be trained to extract certain features from a set of input patterns.
An ANN is configured for a specific application, such as pattern recognition or data classification, through a learning process. Learning in biological systems involves adjustments to the synaptic connections that exist between the neurones.
This figure is taken from my textbook, The Pattern Recognition Basis of Artificial Intelligence. The best fit came at 2700 iterations and then the overfitting began.
Because speech recognition is basically a pattern recognition problem, and because neural networks are good at pattern recognition, many early researchers naturally tried applying neural networks to speech recognition.
The study and use of classification trees are not widespread in the fields of probability and statistical pattern recognition (Ripley, 1996), but classification trees are widely used in applied fields as diverse as medicine (diagnosis), ...
pattern recognition? feeling and intuition? all of the above? can we quantify intelligence and what makes it up?
For instance, Artificial Neural Networks have been used successfully in visual pattern recognition, even human faces and complex industrial components can be differentiated.
The TouchPad also takes advantage of capacitance-sensing technology, but the company has extended the pad's capabilities with proprietary algorithms for pattern recognition and adaptive analog VLSI technology.
Pattern recognition, i.e. recognizing handwritten characters, e.g. the current version of Apple's Newton uses a neural net
Medicine, i.e. storing medical records based on case information
Speech production: reading text aloud (NETtalk) ...
neural network a computational network, often for pattern recognition, composed of mathematically defined elements that are thought to approximate the working of biological neurons; often composed of a layer that receives and organizes inputs, ...
Theoretical pillars of machine learning for complex pattern recognition and classification
Expectation-maximization (EM) algorithms and support vector machines (SVM)
Probabilistic decision-based neural networks (PDNNs) for face biometrics ...
The first of these were in visual pattern recognition and speech recognition. In addition, recent programs for text-to-speech have utilized ANNs. Many handwriting analysis programs (such as those used in popular PDAs) are powered by ANNs.
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If you read only one book on neural networks, that book is recommended to be Neural Networks for Pattern Recognition, CM Bishop (1995), Oxford University Press.
Fuzzy c-means (FCM) is a method of clustering which allows one piece of data to belong to two or more clusters. This method (developed by Dunn in 1973 and improved by Bezdek in 1981) is frequently used in pattern recognition.
Certain sets of pixels are to be interpreted as representing the digit "3", while some other sets of pixels are to be interpreted as representing the digit "5" (pattern recognition).
It may be a stock value prediction for certain circumstances (Forecasting), a risk for a new loan application, a local weather warning or the identification of a person in a new picture (Pattern Recognition).
Our wetware, without the technology, is good at interacting with the world and at pattern recognition. For anything else we need the technology. If we built a Terminator that had only the abilities of our naked brains it would be plain boring.
ANN attempts to simulate some of the neurological processing ability of the biological brain such as learning and drawing conclusion from experience. Therefore, the problems handled by ANN can be quite varied like-pattern recognition, ...
 See also: Neural network, Knowledge, Machine learning, Artificial intelligence, Data mining
  
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