Supervised learning strategies in multi-classifier systems

Impedovo, Donato; Pirlo, G.; Barbuzzi, Donato

doi:10.1109/isspa.2012.6310470

Cited by 5 publications

(2 citation statements)

References 21 publications

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“…In the experimental session, a multi-expert system for handwritten digit recognition has been considered [6,8] and the CEDAR Database of handwritten digits has been used [13]. In this case P={x k | k=1,2,…,20351} (classes from "0" to "9").…”

Section: Cedar Databasementioning

confidence: 99%

“…More specifically this paper proposes to select those samples, to be used for retraining specific experts of the set, misclassified by the multi-expert system [6,7,8,9]. This approach is compared to situation in which the entire new dataset is used for learning as well as the case in which specific samples are selected by the individual classifier.…”

Section: Introductionmentioning

confidence: 99%

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Learning Iterative Strategies in Multi-Expert Systems Using SVMs for Digit Recognition

Barbuzzi

Impedovo

Mangini

et al. 2013

Image Analysis and Processing – ICIAP 2013

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Abstract. This paper presents three different learning iterative strategies, in a multi-expert system. In first strategy entire new dataset is used. In second strategy each single classifier selects new samples starting from those on which it performs a misclassification. Finally, the collective behavior of classifiers is studied to select the most profitable samples for knowledge base updating. The experimental results provide a comparison of three approaches under different operating conditions and feedback process. A classifier SVM and four different combination techniques were used by considering the CEDAR (handwritten digit) database. It is shown how results depend by the iterations on the feedback process, as well as by the specific combination decision schema and by data distribution.

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Section: Cedar Databasementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Learning Iterative Strategies in Multi-Expert Systems Using SVMs for Digit Recognition

Barbuzzi

Impedovo

Mangini

et al. 2013

Image Analysis and Processing – ICIAP 2013

Self Cite

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Learning Strategies for Knowledge-Base Updating in Online Signature Verification Systems

Pirlo

Impedovo

Barbuzzi

2013

New Trends in Image Analysis and Processing – ICIAP 2013

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

Barbuzzi

Pirlo

Uchida

et al. 2015

2015 13th International Conference on Document Analysis and Recognition (ICDAR)

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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

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Supervised learning strategies in multi-classifier systems

Cited by 5 publications

References 21 publications

Learning Iterative Strategies in Multi-Expert Systems Using SVMs for Digit Recognition

Learning Iterative Strategies in Multi-Expert Systems Using SVMs for Digit Recognition

Learning Strategies for Knowledge-Base Updating in Online Signature Verification Systems

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

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