A study on Neural Network Pattern Recognition
Monisha Nagpal
A Study on Neural Network Pattern Recognition
Abstract
applications, such as data mining, web
Among the various traditional approaches of
searching, retrieval of multimedia data, face
pattern recognition the statistical approach
recognition, and cursive handwriting
has been most intensively studied and used
recognition, require robust and efficient
in practice. More recently, the addition of
pattern recognition techniques. The
neural network techniques theory have been
objective of this synopsis paper is to
receiving significant attention. The design of
summarize and compare some of the well-
a recognition system requires careful
known methods used in various stages of a
attention to the following issues: definition
pattern recognition system using NN and
of pattern classes, sensing environment,
identify research topics and applications
pattern representation, feature extraction and
which are at the forefront of this exciting
selection, cluster analysis, classifier design
and challenging field.
and learning, selection of training and test
Keywords: Pattern Recognition, correlation,
samples, and performance evaluation. In
Neural Network.
spite of almost 50 years of research and
INTRODUCTION
development in this field, the general
A pattern is an entity, vaguely defined, that
problem of recognizing complex patterns
could be given a name, e.g.
with arbitrary orientation, location, and scale
Fingerprint image,
remains unsolved. New and emerging
Handwritten word,
Human face,
Pattern recognition techniques are concerned
Speech signal,
with the theory and algorithms of putting
IDNA sequence.
abstract objects, e.g., measurements made
on physical objects, into categories.
Pattern recognition is the study of how
Typically the categories are assumed to be
machines can observe the environment,
known in advance, although there are
learn to distinguish patterns of interest,
techniques to learn the categories
Make sound and reasonable decisions about
(clustering). Methods of pattern recognition
the categories of the patterns.
are useful in many applications such as
The term neural network was traditionally
information retrieval, data mining, document
used to refer to a network or circuit of
image analysis etc.
neurons. The modern usage of the term often
The term pattern recognition can be done by
refers to artificial neural networks, which
the using of Bayesian probability theorem.
are composed of artificial neurons or nodes.
The recognition of the pattern is summarized
Thus the term has two distinct usages. To
by the diagram shown below.
provide an easier understanding of neural
networks, we will begin by telling about the
natural (biological) neural network (the
brain), since the artificial neural network is
actually a mathematic model of the brain.
DATA ACQUISITION AND SENSING:
Measurements of physical variables.
Important issues: bandwidth, resolution,
PATTERN RECOGNITION SYSTEM
sensitivity, distortion, SNR, latency, etc.
The pattern recognition in neural network
Pre-processing:
includes a Pattern Recognition System
Removal of noise in data.
which is used for extraction of the patterns,
Isolation of patterns of interest from the
can be explained by the following diagram
background.
and description.
Feature extraction:
Finding a new representation in terms of
features.
Model learning and estimation:
Learning a mapping between features and
Feature Selection: the next step in the design
pattern groups and categories.
cycle includes is the select features which
Classification:
extract features as Domain dependence and
Using features and learned models to assign
prior information, Computational cost and
a pattern to a category.
feasibility, Discriminative features(Similar
Post-processing:
values for similar patterns, Different values
Evaluation of confidence in decisions.
for different patterns),Invariant features with
Exploitation of context to improve
respect to translation, rotation and scale,
performance.
Robust features with respect to occlusion,
distortion, deformation, and variations in
DESIGN CYCLE
environment.
The design cycle of the pattern recognition
Model selection: the next level is the
is the flow diagram in which the data to be
selection of model which includes the
recognized is flowed and recognized.
domain dependence and prior information,
Definition of design criteria, Parametric vs.
non-parametric models, Handling of missing
features, Computational complexity, Types
Data collection: it is the process of
of models: templates, decision-theoretic or
collecting training and testing data. It even
statistical, syntactic or structural, neural, and
helps us to know
hybrid.
the adequately large
and representative set of samples including
Training: The next phase is the training
the data to be recognized.
phase which includes the ways in which we
learn the rule from data, even differentiate in
is that the event will occur when a random
supervised and unsupervised learning. The
experiment is performed
Supervised learning is one in which a
teacher provides a category label or cost for
each pattern in the training set. Whereas
unsupervised learning is the system forms
clusters or natural groupings of the input
Conditional Probability
patterns. Even the training includes the
If A and B are two events, the probability of
Reinforcement learning which is no desired
event A when we already know that event B
category is given but the teacher provides
has occurred P[A|B] is defined by the
feedback to the system such as the decision
relation
is right or wrong.
Evaluation: the last phase is the evaluation
phase which determines the performance
with training samples and how the
performance is predicted with future data.
Even with this it includes the problems of
over fitting and generalization.
P[A|B] is read as the “conditional
probability of A conditioned on B”,
or simply the “probability of A given B”
Bayes Theorem
Bayes Theorem is definitely the
Fundamental relationship in
PROBABILITY
Probabilities are numbers assigned to events
that indicate “how likely” it
Statistical Pattern Recognition which is
obtained as
Given B1, B2… BN, a partition of the
P(x) a normalization constant that does not
sample space S. Suppose that event A
affect the decision
occurs; what is the probability of event Bj
Using the definition of conditional
CONCLUSION
probability and the Theorem of total
Pattern recognition is a human activity that
probability we obtain
we try to imitate by mechanical means.
There are no physical laws that assign
observations to classes. It is the human
consciousness that groups observations
For pattern recognition, Bayes Theorem can
together. Although their connections and
be expressed a
inter-relations are often hidden, by the
attempt of imitating this process, some
understanding might be gained. The human
Where ωj is the ith class and x is the feature
process of learning patterns from examples
vector
may follow along the lines of trial and error.
Each term in the Bayes Theorem has a
It has, however, to be strongly doubted
special name as
whether statistics play an important role in
P (ωi) Prior probability (of class ωi) P (ωi|x)
this process. Estimating probabilities,
Posterior Probability (of class ωi given the
especially in multi-variate situations is not
observation x)
very intuitive for majority of people.
P (x|ωi) Likelihood (conditional prob. of x
Moreover, the large amount of examples
given class ωi)
needed to build a reliable classifier by
statistical means is much larger than it is
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