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.