Application of EFUNN for the classification of handwritten digits
Handwritten digits classification has many useful applications. This has prompted decades of research into algorithms to produce an effective system of classifying handwritten images into text. Image processing and feature extraction play a large role in this process. An intelligent system is one,...
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
International Journal of Computers, Systems and Signals
2004
|
| Subjects: | |
| Online Access: | http://irep.iium.edu.my/38192/ http://irep.iium.edu.my/38192/1/Application_of_EFUNN_for_the_Classification_of_Handwritten_Digits.pdf |
| _version_ | 1848781558093185024 |
|---|---|
| author | Geok, See Ng Murali, T. Shi, Dingding Abdul Rahman, Abdul Wahab |
| author_facet | Geok, See Ng Murali, T. Shi, Dingding Abdul Rahman, Abdul Wahab |
| author_sort | Geok, See Ng |
| building | IIUM Repository |
| collection | Online Access |
| description | Handwritten digits classification has many useful applications. This has prompted decades of research into
algorithms to produce an effective system of classifying handwritten images into text. Image processing and feature extraction play a large role in this process. An intelligent system is one, which is taught, and one, which uses this learning for classification effectively. The neuro-fuzzy model of Evolving Fuzzy Neural Network (EFuNN) is used for this purpose. This paper aims to analyse and obtain the optimal number of features that will produce the most effective classification using EFuNN. |
| first_indexed | 2025-11-14T15:51:28Z |
| format | Article |
| id | iium-38192 |
| institution | International Islamic University Malaysia |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-14T15:51:28Z |
| publishDate | 2004 |
| publisher | International Journal of Computers, Systems and Signals |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | iium-381922014-09-12T01:34:41Z http://irep.iium.edu.my/38192/ Application of EFUNN for the classification of handwritten digits Geok, See Ng Murali, T. Shi, Dingding Abdul Rahman, Abdul Wahab T Technology (General) Handwritten digits classification has many useful applications. This has prompted decades of research into algorithms to produce an effective system of classifying handwritten images into text. Image processing and feature extraction play a large role in this process. An intelligent system is one, which is taught, and one, which uses this learning for classification effectively. The neuro-fuzzy model of Evolving Fuzzy Neural Network (EFuNN) is used for this purpose. This paper aims to analyse and obtain the optimal number of features that will produce the most effective classification using EFuNN. International Journal of Computers, Systems and Signals 2004 Article PeerReviewed application/pdf en http://irep.iium.edu.my/38192/1/Application_of_EFUNN_for_the_Classification_of_Handwritten_Digits.pdf Geok, See Ng and Murali, T. and Shi, Dingding and Abdul Rahman, Abdul Wahab (2004) Application of EFUNN for the classification of handwritten digits. International Journal of Computers, Systems and Signals, 5 (2). pp. 27-35. http://www.informatik.uni-trier.de/~ley/db/journals/ijcss/ijcss5.html |
| spellingShingle | T Technology (General) Geok, See Ng Murali, T. Shi, Dingding Abdul Rahman, Abdul Wahab Application of EFUNN for the classification of handwritten digits |
| title | Application of EFUNN for the classification of handwritten digits |
| title_full | Application of EFUNN for the classification of handwritten digits |
| title_fullStr | Application of EFUNN for the classification of handwritten digits |
| title_full_unstemmed | Application of EFUNN for the classification of handwritten digits |
| title_short | Application of EFUNN for the classification of handwritten digits |
| title_sort | application of efunn for the classification of handwritten digits |
| topic | T Technology (General) |
| url | http://irep.iium.edu.my/38192/ http://irep.iium.edu.my/38192/ http://irep.iium.edu.my/38192/1/Application_of_EFUNN_for_the_Classification_of_Handwritten_Digits.pdf |