Earticle

초록

영어

Among the various traditional approaches of pattern recognition the statistical approach has been most intensively studied and used in practice. More recently, the addition of artificial neural network techniques theory have been receiving significant attention. The design of a recognition system requires careful attention to the following issues: definition of pattern classes, sensing environment, pattern representation, feature extraction and selection, cluster analysis, classifier design and learning, selection of training and test samples, and performance evaluation. In spite of almost 50 years of research and development in this field, the general problem of recognizing complex patterns with arbitrary orientation, location, and scale remains unsolved. New and emerging applications, such as data mining, web searching, retrieval of multimedia data, face recognition, and cursive handwriting recognition, require robust and efficient pattern recognition techniques. The objective of this review paper is to summarize and compare some of the well-known methods used in various stages of a pattern recognition system using ANN and identify research topics and applications which are at the forefront of this exciting and challenging field.

목차

Abstract
 1. Introduction
 2. Pattern Recognition
 3. Artificial Neural Networks
 4. Works done
 5. Conclusions
 6. References

키워드

저자

• Jayanta Kumar Basu [ Computer Science and Engineering Department Heritage Institute of Technology Kolkata, India ]
• Debnath Bhattacharyya [ Multimedia Engineering Department Hannam University Daejeon, Korea ]
• Tai-hoon Kim [ Multimedia Engineering Department Hannam University Daejeon, Korea ] Corresponding Author

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  International Journal of Software Engineering and Its Applications

• 간기

  월간

• pISSN

  1738-9984

• 수록기간

  2008~2016

• 등재여부

  SCOPUS

• 십진분류

  KDC 505 DDC 605