BBM NSPR - 2008 - b. balamanigandan, www.bbminfo.com (BBM National Seminar)
Venue: bbm soft solution
|Event Date/Time: Dec 06, 2008||End Date/Time: Dec 07, 2008|
|Registration Date: Nov 06, 2008|
|Early Registration Date: Nov 06, 2008|
|Abstract Submission Date: Oct 06, 2008|
|Paper Submission Date: Nov 06, 2008|
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, 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 (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. 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 Bayesian classifiers to much more powerful 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.
by b. balamanigandan, Director & Head in bbm groups of Pvt. Ltd., www.bbminfo.com , Email : firstname.lastname@example.org