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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Kittler, J., Hatef, M., Duin, R.P.W., Matias, J.: On combining classifiers. IEEE Trans. on PAMI 20(3), 226–239 (1998)
Suen, C.Y., Nadal, C., Legault, R., Mai, T.A., Lam, L.: Computer Recognition of unconstrained handwritten numerals. Proc. IEEE 80(7), 1162–1180 (1992)
Liu, C.L., Nakashima, K., Sako, H., Fujisawa, H.: Handwritten digit recognition: benchmarking of state-of-the-art techniques. Pattern Recognition 36(10), 2271–2285 (2003)
Pirlo, G., Impedovo, D.: Fuzzy-Zoning-Based Classification for Handwritten Characters. IEEE Trans. on Fuzzy Systems 19(4), 780–785 (2011)
Suen, C.Y., Tan, J.: Analysis of errors of handwritten digits made by a multitude of classifiers. Pattern Recognition Letters 26(3), 369–379 (2005)
Impedovo, D., Pirlo, G.: Updating Knowledge in Feedback-based Multi-Classifier Systems. In: Proc. of ICDAR, pp. 227–231 (2011)
Barbuzzi, D., Impedovo, D., Pirlo, G.: Feedback-Based Strategies In Multi-Expert Systems. In: Sesto Convegno del Gruppo Italiano Ricercatori in Pattern Recognition (2012)
Impedovo, D., Pirlo, G., Barbuzzi, D.: Supervised Learning Strategies in Multi-Classifier Systems. In: Proceedings of 11th International Conference on Information Science, Signal Processing and their Applications (ISSPA 2012), pp. 1215–1220 (2012)
Barbuzzi, D., Impedovo, D., Pirlo, G.: Benchmarking of Update Learning Strategies on Digit Classifier Systems. In: Proceedings of the 13th International Conference on Frontiers in Handwriting Recognition, pp. 35–40 (2012)
Freud, Y., Schapire, R.E.: Decision-theoretic generalization of on-line learning and an application to boosting. J. of Computer and System Sciences 55(1), 119–139 (1997)
Schapire, R.E.: The strength of weak learnability. Machine Learning 5(2), 197–227 (1990)
Polikar, R.: Bootstrap-Inspired Techniques in Computational Intelligence. IEEE Signal Processing Magazine 24(4), 59–72 (2007)
Hull, J.: A database for handwritten text recognition research. IEEE T-PAMI 16(5), 550–554 (1994)
Impedovo, S., Modugno, R., Ferrante, A., Pirlo, G.: Zoning Methods for Hand-written Character Recognition: An Overview. In: Proceedings of the 12th International Conference on Frontiers in Handwriting Recognition (ICFHR 2012), Kolkata, India, November 16-18, pp. 329–334 (2010)
Impedovo, D., Modugno, R., Pirlo, G.: New Advancements in Zoning-Based Recognition of Handwritten Characters. In: Proc. XIII International Conference on Frontiers in Handwriting Recognition (ICFHR 2012), Monopoli, Bari, Italy, September 18-20, pp. 661–665 (2012)
Impedovo, S., et al.: Feature Membership Functions in Voronoi-Based Zoning. In: Serra, R., Cucchiara, R. (eds.) AI*IA 2009. LNCS, vol. 5883, pp. 202–211. Springer, Heidelberg (2009)
Impedovo, S., Modugno, R., Pirlo, G.: Analysis of Membership Functions for Voronoi-based Classification. In: Proceedings of the 12th Interational Conference on Frontiers in Handwriting Recognition (ICFHR 2012), November 16-18, pp. 220–225. IEEE Computer Society Press, Kolkata (2010)
Pirlo, G., Impedovo, D.: Adaptive Membership Functions for Hand-Written Character Recognition by Voronoi-based Image Zoning. IEEE Transactions on Image Processing 21(9), 3827–3837 (2012)
Impedovo, S., Pirlo, G.: Tuning between Exponential Functions and Zones for Membership Functions Selection in Voronoi-based Zoning for Handwritten Character Recognition. In: Proc. of the 11th International Conference on Document Analysis and Recognition (ICDAR 2011), September 18-21, pp. 997–1001. IEEE Computer Society, Beijing (2011) ISBN: 978-0-7695-4520-2
Impedovo, D., Modugno, R., Pirlo, G.: Score Normalization by Dynamic Time Warping. In: Proceedings of the International Conference on Computational Intelligence for Measurement Systems and Applications (CIMSA), Taranto, Italy, September 6-8, pp. 82–85. IEEE Computer Society Press, Taranto (2010) ISBN: 978-1-4244-7229-1
Pirlo, G., Impedovo, D.: Adaptive Score Normalization for Multi-Classifier Systems. IEEE Signal Processing Letters 19(12), 837–840 (2012) ISSN: 1070-9908
Impedovo, D., Pirlo, G., Sarcinella, L., Stasolla, E.: Artificial Classifier Generation for Multi-Expert System Evaluation. In: Proceedings of the 12th International Conference on Frontiers in Handwriting Recognition (ICFHR 2012), November 16-18, pp. 42–426. IEEE Computer Society Press, Kolkata (2010) ISBN: 978-0-7695-4221-8
Bovino, L., Dimauro, G., Impedovo, S., Lucchese, M.G., Modugno, R., Pirlo, G., Salzo, A., Sarcinella, L.: On the Combination of Abstract-Level Classifiers. International Journal on Document Analysis and Recognition 6, 42–54 (2003) ISSN 1433-2833
Frinken, V., Bunke, H.: Evaluating Retraining Rules for Semi-Supervised Learning in Neural Network Based Cursive Word Recognition. In: Proc. of ICDAR, pp. 31–35 (2009)
Frinken, V., Fischer, A., Bunke, H., Fornes, A.: Co-Training for Handwritten Word Recognition. In: Proc. of ICDAR, pp. 314–318 (2011)
Blum, A., Mitchell, T.: Combining Labeled and Unlabeled Data with Co-Training. In: ACM Proc. of COLT, pp. 92–100 (1998)
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Barbuzzi, D., Impedovo, D., Mangini, F.M., Pirlo, G. (2013). Learning Iterative Strategies in Multi-Expert Systems Using SVMs for Digit Recognition. In: Petrosino, A. (eds) Image Analysis and Processing – ICIAP 2013. ICIAP 2013. Lecture Notes in Computer Science, vol 8156. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-41181-6_13
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DOI: https://doi.org/10.1007/978-3-642-41181-6_13
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