Loading [a11y]/accessibility-menu.js
Combining SVM classifiers for handwritten digit recognition | IEEE Conference Publication | IEEE Xplore
Scheduled Maintenance: On Tuesday, 25 February, IEEE Xplore will undergo scheduled maintenance from 1:00-5:00 PM ET (1800-2200 UTC). During this time, there may be intermittent impact on performance. We apologize for any inconvenience.

Combining SVM classifiers for handwritten digit recognition


Abstract:

We investigate the advantages and weaknesses of various decision fusion schemes using statistical and rule-based reasoning. The cooperation schemes are applied on two SVM...Show More

Abstract:

We investigate the advantages and weaknesses of various decision fusion schemes using statistical and rule-based reasoning. The cooperation schemes are applied on two SVM (Support Vector Machine) classifiers performing classification tasks on two feature families referenced as structural and statistical features. The obtained results show that it is difficult to exceed the recognition rate of a single classifier applied straightforwardly on both feature families as one set. The rule based cooperation schemes enable an easy and efficient implementation of various rejection criteria. On the other hand, the statistical cooperation schemes provide higher recognition rates and offer possibility for fine-tuning of the recognition versus the reliability tradeoff.
Date of Conference: 11-15 August 2002
Date Added to IEEE Xplore: 10 December 2002
Print ISBN:0-7695-1695-X
Print ISSN: 1051-4651
Conference Location: Quebec City, QC, Canada

References

References is not available for this document.