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

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

研究成果: Chapter in Book/Report/Conference proceedingConference contribution

抄録

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.

本文言語英語
ホスト出版物のタイトル13th IAPR International Conference on Document Analysis and Recognition, ICDAR 2015 - Conference Proceedings
出版社IEEE Computer Society
ページ101-105
ページ数5
ISBN(電子版)9781479918058
DOI
出版ステータス出版済み - 11 20 2015
イベント13th International Conference on Document Analysis and Recognition, ICDAR 2015 - Nancy, フランス
継続期間: 8 23 20158 26 2015

出版物シリーズ

名前Proceedings of the International Conference on Document Analysis and Recognition, ICDAR
2015-November
ISSN(印刷版)1520-5363

その他

その他13th International Conference on Document Analysis and Recognition, ICDAR 2015
Countryフランス
CityNancy
Period8/23/158/26/15

All Science Journal Classification (ASJC) codes

  • Computer Vision and Pattern Recognition

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