Effective partitioning of input domains for ALM algorithm

Author

Afrakoti, I.E.P. ; Ghaffari, Aboozar ; Shouraki, Saeed Bagheri

Author_Institution

Dept. of Electr. Eng., Sharif Univ. of Technol., Tehran, Iran

fYear

2013

fDate

6-8 March 2013

Firstpage

1

Lastpage

5

Abstract

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.

Keywords

inference mechanisms; learning systems; pattern recognition; ALM algorithm; IDS algorithm; active learning method; feature extraction; ink drop spread algorithm; input domain partitioning; pattern recognition; pattern-based algorithm; soft computing; system modeling; Classification algorithms; Computational modeling; Equations; Feature extraction; Ink; Mathematical model; Partitioning algorithms; Active learning method (ALM); fuzzy inference algorithm; ink drop spread (IDS);

fLanguage

English

Publisher

ieee

Conference_Titel

Pattern Recognition and Image Analysis (PRIA), 2013 First Iranian Conference on

Conference_Location

Birjand

Print_ISBN

978-1-4673-6204-7

Type

conf

DOI

10.1109/PRIA.2013.6528437

Filename

6528437