Artificial Neural Networks in Pattern Recognition: 4th IAPR by Ahmed Al-Ani, Amir F. Atiya (auth.), Friedhelm Schwenker,

By Ahmed Al-Ani, Amir F. Atiya (auth.), Friedhelm Schwenker, Neamat El Gayar (eds.)

This publication constitutes the refereed lawsuits of the 4th IAPR TC3 Workshop, ANNPR 2010, held in Cairo, Eqypt, in April 2010. The 23 revised complete papers provided have been conscientiously reviewed and chosen from forty two submissions. the main issues of ANNPR are supervised and unsupervised studying, function choice, development popularity in sign and snapshot processing, and purposes in info mining or bioinformatics.

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Extra info for Artificial Neural Networks in Pattern Recognition: 4th IAPR TC3 Workshop, ANNPR 2010, Cairo, Egypt, April 11-13, 2010. Proceedings

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Technical Report DSL TR-08-01 (2008) 21. : Bagging predictors. Machine Learning 24(2), 123–140 (1996) 22. html 23. php A New Monte Carlo-Based Error Rate Estimator Ahmed Hefny and Amir Atiya Cairo University Faculty of Engineering Computer Engineering Department Abstract. Estimating the classification error rate of a classifier is a key issue in machine learning. Such estimation is needed to compare classifiers or to tune the parameters of a parameterized classifier. Several methods have been proposed to estimate error rate, most of which rely on partitioning the data set or drawing bootstrap samples from it.

4 EˆGMCP − E −1 0 10 10 d Fig. 1. Effect of degree of class separation on error rate estimation. Top-left: EsˆG − E and timation of the true error rate E. Top-right: Error estimation bias (E ˆ EM CP − E). Bottom-left: Combination weight as calculated by iteratively applying equations 5 and 6 for 10 iterations. Bottom-right: Bias of the final error estimate ˆGM CP − E) All depicted quantities are averaged over 100 trials. (E low separation and the error rate is high while EG is a good estimator when there is high separation and the error rate is low.

Machine Learning 65, 31–78 (2006) 13. : A hybrid Bayesian network learning method for constructing gene networks. Computational Biology and Chemistry 31, 361–372 (2007) 14. : Causation, Prediction, and search. Springer, New York (1993) 15. : Learning Belief Networks from Data: An Information theory Based Approach. In: Proceedings of the Sixth ACM International Conference on Information and Knowledge Management, pp. 325–331 (1997) 16. : Time and Sample Efficient Discovery of Markov Blankets and Direct Causal Relations.

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