13-08-2012, 02:32 PM
Paradigms for Pattern Recognition
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Different Paradigms for Pattern Recognition
• There are several paradigms in use to solve the pattern recognition
problem.
• The two main paradigms are
1. Statistical Pattern Recognition
2. Syntactic Pattern Recognition
• Of the two, the statistical pattern recognition has been more popular
and received a major attention in the literature.
• The main reason for this is that most of the practical problems in this
area have to deal with noisy data and uncertainty and statistics and
probability are good tools to deal with such problems.
• On the other hand, formal language theory provides the background for
syntactic pattern recognition. Systems based on such linguistic tools,
more often than not, are not ideally suited to deal with noisy envi-
ronments. However, they are powerful in dealing with well-structured
domains. Also, recently there is a growing interest in statistical pattern
recognition because of the influence of statistical learning theory.
• This naturally prompts us to orient material in this course towards
statistical classification and clustering.
Statistical Pattern Recognition
• In statistical pattern recognition, we use vectors to represent patterns
and class labels from a label set.
• The abstractions typically deal with probability density/distributions
of points in multi-dimensional spaces, trees and graphs, rules, and vec-
tors themselves.
• Because of the vector space representation, it is meaningful to talk of
subspaces/projections and similarity between points in terms of dis-
tance measures.
2
• There are several soft computing tools associated with this notion. Soft
computing techniques are tolerant of imprecision, uncertainty and ap-
proximation. These tools include neural networks, fuzzy systems and
evolutionary computation.
• For example, vectorial representation of points and classes are also
employed by
– neural networks,
– fuzzy set and rough set based pattern recognition schemes.
• In pattern recognition, we assign labels to patterns. This is achieved
using a set of semantically labelled patterns; such a set is called the
training data set. It is obtained in practice based on inputs from ex-
perts.