Similarity-based regularization for semi-supervised learning for handwritten digit recognition

D. Barbuzzi, G. Pirlo, S. Uchida, V. Frinken, D. Impedovo

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

This paper presents an experimental analysis on the use of semi-supervised learning in the handwritten digit recognition field. More specifically, two new feedback-based techniques for retraining individual classifiers in a multi-expert scenario are discussed. These new methods analyze the final decision provided by the multi-expert system so that sample classified with a confidence greater than a specific threshold is used to update the system itself. Experimental results carried out on the CEDAR (handwritten digits) database are presented. In particular, error rate, similarity index and a new correlation score among them are considered in order to evaluate the best retraining rule. For the experimental evaluation, an SVM classifier and five different combination techniques at abstract and measurement level have been used. Finally, the results show that iterating the feedback process, on different multi-expert systems built with the five combination techniques, one retraining rule is winning over the other respect to the best correlation score.

Original languageEnglish
Title of host publication13th IAPR International Conference on Document Analysis and Recognition, ICDAR 2015 - Conference Proceedings
PublisherIEEE Computer Society
Pages101-105
Number of pages5
ISBN (Electronic)9781479918058
DOIs
Publication statusPublished - Nov 20 2015
Event13th International Conference on Document Analysis and Recognition, ICDAR 2015 - Nancy, France
Duration: Aug 23 2015Aug 26 2015

Publication series

NameProceedings of the International Conference on Document Analysis and Recognition, ICDAR
Volume2015-November
ISSN (Print)1520-5363

Other

Other13th International Conference on Document Analysis and Recognition, ICDAR 2015
CountryFrance
CityNancy
Period8/23/158/26/15

All Science Journal Classification (ASJC) codes

  • Computer Vision and Pattern Recognition

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  • Cite this

    Barbuzzi, D., Pirlo, G., Uchida, S., Frinken, V., & Impedovo, D. (2015). Similarity-based regularization for semi-supervised learning for handwritten digit recognition. In 13th IAPR International Conference on Document Analysis and Recognition, ICDAR 2015 - Conference Proceedings (pp. 101-105). [7333734] (Proceedings of the International Conference on Document Analysis and Recognition, ICDAR; Vol. 2015-November). IEEE Computer Society. https://doi.org/10.1109/ICDAR.2015.7333734