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

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

This publication constitutes the refereed court cases of the 4th IAPR TC3 Workshop, ANNPR 2010, held in Cairo, Eqypt, in April 2010. The 23 revised complete papers provided have been rigorously reviewed and chosen from forty two submissions. the foremost issues of ANNPR are supervised and unsupervised studying, characteristic choice, development reputation in sign and picture processing, and functions in facts mining or bioinformatics.

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

Example text

In the above evaluation, we used (2) as the between-class scatter. Instead of (2), we used (15) and compared the difference of the selected features for KDA+BC, but there were not much difference between the two. 95 were the same. Table 3 shows the feature selection time for the four methods. In each problem the shortest time is shown in boldface. For the thyroid data set, we measured the feature selection time confining the value of C in C = [10000, 50000, 100000]. By introducing block deletion two to five times speedup was realized.

37–47, 2010. c Springer-Verlag Berlin Heidelberg 2010 38 A. Hefny and A. Atiya for a misclassification rate estimator can be decomposed into the two conflicting components of bias and variance. An estimator that is insensitive to the precise locations of the sampled patterns will typically have a low variance and high bias, and the converse is true too. Obtaining an accurate error rate estimator amounts to mastering the right trade-off between bias and variance. , N , where xi is a p-dimensional feature vector, yi is its classification label, and G is the distribution from which (X, Y ) is drawn.

J. Mach. Learn. Res. 5, 1205–1224 (2004) 12. : The max-min hill-climbing Bayesian network structure learning algorithm. 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.

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