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patternrecognition.co.za |
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Home | News | Services | Downloads | Forum | About us | Contact us |
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| Resources Publications Conference papers Source Code Classifier source code Tutorials Pattern recognition tutorials Terminology Definitions of pattern recognition terms Pattern Recognition Applications A summary of pattern recognition applications Artificial Intelligence Interesting articles and discussions regarding artificial intelligence Classification Applet Applet Online implementation of various classifiers Data Set Format Description of the data set format used for the classification applet Example Data Sets Downloadable data set examples Classification Applet Documentation Description of the algorithms used SVM Applet SVM Applet Illustration of SVM classifier
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The
purpose of this web site is to provide resources to experienced
researchers as well as new comers in the fields of pattern recognition,
artificial intelligence, machine learning and other overlapping
research fields. The focus of this site's research is to fully describe the relationships between data characteristics and classifier behaviour, and to develop algorithms that automatically select classifiers and parameters appropriate for a given set or subset of data. Relevant publications are available from the 'Publications' link and other relevant tutorials are available at the 'Tutorials' link. It is clear from the literature that there is no best classifier for all types of problems. Some guidelines have been proposed in the literature for classifier selection. These guidelines, however, do not provide much insight on the specific characteristics of the data that will determine the preference of classifier. An empirical comparison of classifiers on any problem can be performed by using the classification applet available at the 'Applet' link. Pattern recognition is a very active research area which overlaps with various other research fields such as Machine Learning, Artificial Intelligence, Data Mining, Probability Theory, Algebra and Calculus. Source code for various classifiers and other numerical and statistical algorithms are available at the 'Source Code' link. Terminology used in these research fields can be found at the 'Tutorials' link. Classifiers cover a wide range of information processing problems. These problems often have great importance and include speech recognition, classification of handwritten characters, fault detection in machinery, medical diagnosis and many other. A non-exhaustive list of pattern recognition applications is available at the 'Pattern Recognition Applications' link'. Classification algorithms have been applied to various comercial products very succesfully. Commercial Neural Network Classification, Regression, Data Analysis and many other applications are available at the links on the left hand side.
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Recommended Links
UCI Machine Learning Repository Repository of various classification data sets Meta-Level Learning Web site dedicated to meta level learning Weka Machine learning package Yale Machine learning package Statistical Pattern Recognition Toolbox Matlab pattern recognition toolbox libSVM SVM classification package Pattern Recognition Tools Matlab pattern recognition toolbox Opt Neural Network Neural network source code |
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Copyright 2007. www.patternrecognition.co.za |
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