Statistical Pattern Recognition in Artificial intellgence

All key point of 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 vectors themselves.
  • Because of the vector space representation, it is meaningful to talk of subspaces/projections and similarity between points in terms of distance measures.
  • 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 experts.
 Dataset of two classes
Dataset of two classes

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