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Pattern Recognition
What is Pattern Recognition?
 Pattern recognition is a sub-topic of machine
learning. PR is the science that concerns the
description or classification (recognition) of
measurements. It can be defined as:
“The act of taking in raw data and taking an
action based on the category of the data".
“The assignment of a physical object or event to
one of several prespecified categories”.
 A pattern is an object, process or event that can
be given a name.
 A pattern class (or category) is a set of patterns
sharing common attributes and usually
originating from the same source.
 During recognition (or classification) given
objects are assigned to prescribed classes.
 A classifier is a machine which performs
Pattern recognition system
A complete pattern recognition system consists
of : Sensor -gathers the observations to be
classified or described .
 Feature extraction mechanism -computes
numeric or symbolic information from the
observations .
 Classification or description scheme -that
does the actual job of classifying or describing
observations, relying on the extracted features.
Algorithms used by pattern recognition systems
Description task
The description task transforms data collected from the
environment into features.
The description task can involve several different, but
interrelated, activities:
 Preprocessing:-To modify the data
 Feature extraction:-To generate features
-- Elementary features
-- Higher order features
 Feature selection:-To reduce features
Description task (cont.)
The end result of the description task is a
set of features, commonly called a feature
vector which constitutes a representation
of the data.
Classification task
 Uses a classifier to map a feature vector to a
 Such a mapping can be specified by hand or,
more commonly, a training phase is used to
induce the mapping from a collection of feature
vectors known to be representative of the
various groups among which discrimination is
being performed (i.e., the training set).
 Once formulated, the mapping can be used to
assign an identification to each unlabeled
feature vector subsequently presented to the
What makes a ”good” feature vector
Approaches to pattern recognition
There are 2 fundamental approaches
implement a pattern recognition system:
1.Statistical (or decision theoretic):-Statistical
pattern recognition is based on statistical
characterizations of patterns, assuming that the
patterns are generated by a probabilistic
2. Syntactic (or structural):-Syntactical pattern
recognition is based on the structural
interrelationships of features.
Statistical pattern recognition
 It draws from established concepts in statistical
decision theory to discriminate among data
from different groups based upon quantitative
features of the data.
 There are a wide variety of statistical techniques
that can be used within the description task for
feature extraction, ranging from simple
descriptive statistics to complex transformations.
Syntactic pattern recognition
 Syntactic pattern recognition or structural pattern
recognition is a form of pattern recognition, where items
are presented pattern structures which can take into
account more complex interrelationships between
features than simple numerical feature vectors used in
statistical classification.
 It can be used (instead of statistical pattern recognition)
if there is clear structure in the patterns.
 One way to present such structure is strings of a formal
language. In this case differences in the structures of the
classes are encoded as different grammars.
Approaches to pattern recognition
Difference Between Statistical and
Structural Pattern Recognition
Statistical decision theory
Human perception and
Quantative features
Morphological primitives
Fixed no. of features
Variable number of primitives
Ignores feature relationships
Captures primitive relationships
Semantics from feature
Semantics from primitive
Statistical classifiers
Parsing with syntactic
Neural networks pattern recognition
 An “Artificial Neural Network" (ANN), is a
mathematical model or computational model
based on biological neural networks. It consists
of an interconnected group of artificial neurons
and processes information using a connectionist
approach to computation.
 In more practical terms neural networks are nonlinear statistical data modeling tools. They can
be used to model complex relationships
between inputs and outputs or to find patterns in
Neural networks pattern recognition
 Classification is based on the response of a network of
processing units(neurons) to an input stimuli (pattern).
 “Knowledge” is stored in the connectivity and strength
of the synaptic weights.
 NeurPR is a trainable, non-algorithmic, black-box
 NeurPR is very attractive since
-it requires minimum a priori knowledge
-with enough layers and neurons, an ANN can create
any complex decision region.