H. Moga, L. Miron
1)
, and Gh. Pana
2)
Electronics and Computers Department, Transilvania University, Brasov, Romania,
1)
Electronics and Informatics Department, “Henri Coanda” Air Force Academy, Brasov,
Romania,
2)
Electronics and Computers Department, Transilvania University, Brasov, Romania
Email: horatiu.moga@gmail.com
Topic: B
Presentation: P
The purpose of our study is the construction of probabilistic classifier for signal detection. Applications of this classifier could be in various areas like surveillance, biomedical equipments, automation with machine vision, military technology, etc. Naïve Bayes classifier are used for learning machine, and the study tries to determine the posterior probabilities for the present approach.
probabilistic classifier, Bayes, signal detection, Op Amp
We try and build a classifier which will predict whether a ball is red or blue based on their measured size alone. We have two groups of balls red and blue and border between them. A white ball (could be red or blue) could be in one of groups. Depend by the size to evaluate the affiliation.
Fig. 1: Red balls and blue balls are separated by the size
We consider the generative case with two classes. The detector support Gauss distribution for both of them. The balls size is in first class if ray is smaller 2.25 and bigger 2.75 and second

class other else. For first class we calculated the mean µ1=2.5 and standard deviation ϭ1=0.88.
The second class mean is µ2=0.91 and standard deviation ϭ2=3.6. The schematic is presented in
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Fig. 2.
We suppose the uniform distribution for both classes 0.7 for first and 0.5 for second one.
The outcome show us that it verify the initial hypotheses (Fig. 3). The transition band between
(2.25, 2.5) and (2.5, 2.75) decide if white ball is red or blue, and if it is in one class or other one.
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[1]
Christopher M. Bishop,“Pattern Recognition and Machine Learning”, Springer Science+Business
Media, LLC, pg. 181, 2006.
[2] Andrew Webb, “Statistical Pattern Recognition”, Second Edition, John Wiley & Sons Ltd., pp.
123-180, 2000.
[3]
Sebe N., Ira Cohen, Ashutosh Garg, Thomas S. Huang, “Machine Learning in Computer Vision”,
Springer Science+Business Media, LLC, pp. 71, 2005.