Name
Title | ||
|---|---|---|
Input space transformations for multi-classifier systems based on n-tuple classifiers with application to handwriting recognition |
Abstract | ||
|---|---|---|
In this paper we investigate the properties of novel systems for handwritten character recognition which are based on input space transformations to exploit the advantages of multiple classifier structures. These systems provide an effective solution to the problem of utilising the power of n-tuple based classifiers while, simultaneously, addressing successfully the issues of the trade-off between the memory requirements and the accuracy achieved. Utilizing the flexibility offered by multi-classifier schemes we can subsequently exploit this complementarity of different transformations of the original feature space while at the same time decompose it to simpler input spaces, thus reducing the resources requirements of the sn-tuple classifiers used. Our analysis of the observed behaviour based on Mutual Information estimators between the original and the transformed input spaces showed a direct correspondence of the values of this information measure and the accuracy obtained. This suggests Mutual Information as a useful tool for the analysis and design of multi-classifier systems. The paper concludes with a number of comparisons with results on the same data set achieved by a diverse set of classifiers. Our findings clearly demonstrate the significant gains that can be obtained, simultaneously in performance and memory space reduction, by the proposed systems. |
Year | DOI | Venue |
|---|---|---|
2003 | 10.1007/3-540-44938-8_36 | Multiple Classifier Systems |
Keywords | Field | DocType |
multi-classifier scheme,input space,mutual information,handwriting recognition,memory space reduction,input space transformation,diverse set,mutual information estimator,n-tuple classifier,original feature space,simpler input space,memory requirement,multi-classifier system,feature space | Random subspace method,Computer science,Image processing,Handwriting recognition,Artificial intelligence,Classifier (linguistics),Distributed computing,Feature vector,Pattern recognition,Tuple,Mutual information,Machine learning,Chain code | Conference |
Volume | ISSN | ISBN |
2709 | 0302-9743 | 3-540-40369-8 |
Citations | PageRank | References |
1 | 0.38 | 10 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| K. Sirlantzis | 1 | 36 | 3.39 |
| Sanaul Hoque | 2 | 93 | 13.16 |
| M. C. Fairhurst | 3 | 185 | 29.50 |