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College of Science & Technology
Dep. Of Computer Science & IT
BCs of Information Technology
Data Mining
Chapter 4_4: Classification Methods
(Examples)
2013
Prepared by: Mahmoud Rafeek Al-Farra
www.cst.ps/staff/mfarra
Course’s Out Lines
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Introduction
Data Preparation and Preprocessing
Data Representation
Classification Methods
Evaluation
Clustering Methods
Mid Exam
Association Rules
Knowledge Representation
Special Case study : Document clustering
Discussion of Case studies by students
Out Lines
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Naïve Bayesian Classifiers
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Artificial Neural Networks
Naïve Bayesian Classifiers
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A Bayes classifier is a simple probabilistic
classifier based on applying Bayes' theorem (from
Bayesian
statistics)
with
strong
(naive)
independence assumptions.
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A more descriptive term for the underlying
probability model would be "independent feature
model".
What is ANN?
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An artificial neural network can be defined as a
model of reasoning based on the human brain.
Synapse
Axon
Soma
Synapse
Dendrites
Axon
Soma
Dendrites
Synapse
What is ANN?
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Analogy between biological and artificial neural networks
Artificial Neural Networks
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ANNs can be defined as a model of reasoning
based on the human brain.
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A NN is a system of processing units, connections
and weights associated with the connections which
propagates activation from its input units to its
output units, augmented by a learning rule.
Artificial Neural Networks
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ANN can be viewed as weighted directed graphs in
which artificial neurons are nodes and directed edges
labeled with weights are connections between neuron
outputs and neuron inputs.
Simple computing element in ANN
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Artificial Neural Networks
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The behavior of the network is determined by the
combination of its architecture and its set of
weights which is known as the learning of network.
X
i
ij
Wi
Classification of ANN architectures
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Feedforward neural network
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Feedback neural network
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Next …
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Evaluation
Thanks
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