Effective partitioning of input domains for ALM algorithm

This paper presents a new and simple algorithm for partitioning the input domain for implementation of Active Learning Method (ALM) algorithm. ALM is a pattern-based algorithm for soft computing which uses the Ink Drop Spread (IDS) algorithm as its main engine for feature extraction. In this paper a...

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Bibliographic Details
Published in2013 First Iranian Conference on Pattern Recognition and Image Analysis (PRIA) pp. 1 - 5
Main Authors Afrakoti, I. E. P., Ghaffari, A., Shouraki, S. B.
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.03.2013
Subjects
Active learning method (ALM)
Classification algorithms
Computational modeling
Equations
Feature extraction
fuzzy inference algorithm
Ink
ink drop spread (IDS)
Mathematical model
Partitioning algorithms
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Summary:This paper presents a new and simple algorithm for partitioning the input domain for implementation of Active Learning Method (ALM) algorithm. ALM is a pattern-based algorithm for soft computing which uses the Ink Drop Spread (IDS) algorithm as its main engine for feature extraction. In this paper a simple algorithm is introduced with a few computation cost. In order to evaluate the performance of the proposed algorithm, it is applied to two applications, system modeling and pattern recognition. Simulation results show the effectiveness of our algorithm in specifying the appropriate points for dividing the inputs domains.
ISBN:1467362042
9781467362047
DOI:10.1109/PRIA.2013.6528437