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 themes of ANNPR are supervised and unsupervised studying, characteristic choice, development acceptance in sign and snapshot processing, and purposes in info mining or bioinformatics.
Read or Download Artificial Neural Networks in Pattern Recognition: 4th IAPR TC3 Workshop, ANNPR 2010, Cairo, Egypt, April 11-13, 2010. Proceedings PDF
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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
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.
SU = 2 IG(X|Y ) H(X)H(Y ) (2) IG(X, Y ) = H(X) − H(X|Y ) (3) H(X) = − (4) P (xi )log2 P (xi ) i where IG(X|Y ) is the Information Gain of X after observing variable Y . H(X) and H(Y ) are the entropy of variable X and Y , respectively. P (xi ) is the probability of variable x. SU is the modiﬁed version of Information Gain that has range between 0 and 1. FCBF removes irrelevant features by ranking correlation (SU) between feature and class. If SU between feature and class equal to 1, it means that this feature is completely related to that class.
Toward integrating feature selection algorithms for classiﬁcation and clustering. IEEE Transactions on Knowledge and Data Engineering 17(4), 491–502 (2005) 2. : A review of feature selection techniques in bioinformatics. Bioinformatics 23(19), 2507–2517 (2007) 3. : Relevance and Redundancy Analysis for Ensemble Classiﬁers. In: Perner, P. ) Machine Learning and Data Mining in Pattern Recognition. LNCS, vol. 5632, pp. 206–220. Springer, Heidelberg (2009) 36 R. Duangsoithong and T. Windeatt 4. : Causal Feature Selection.