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.)

ISBN-10: 3642121594

ISBN-13: 9783642121593

This publication constitutes the refereed complaints of the 4th IAPR TC3 Workshop, ANNPR 2010, held in Cairo, Eqypt, in April 2010. The 23 revised complete papers awarded have been conscientiously reviewed and chosen from forty two submissions. the key issues of ANNPR are supervised and unsupervised studying, characteristic choice, trend acceptance in sign and photograph processing, and functions in information mining or bioinformatics.

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Additional 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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For each setup, the first row shows the bias, the second shows the variance and the third shows the MSE. 004 Table 6. 1389 A New Monte Carlo-Based Error Rate Estimator 6 47 Conclusions In this work we developed a complementary pair of error rate estimators that utilize a generative estimator and a posterior estimator. We proposed an iterative combination of the two estimators. The iterative solution was based on the fact that the best combination depends on a hidden parameter (true error rate). We are planning to study the possibility of integrating more visible and hidden parameters such as number of samples and amount of overfitting to get a more reliable estimator.

PC algorithm [14], is a pioneer, prototype and well-known global algorithm of Constraint-Based approach for causal discovery. Three Phase Dependency Analysis (TPDA or PowerConstructor) [15] is another global Constraint-Based algorithm that uses mutual information to search and test for CI test instead of using G2 Statistics test as in PC algorithm. However, both PC and TPDA algorithm use global search to learn from the complete network that can not scale up to more than few hundred features (they can deal with 100 and 255 features for PC and TPDA, respectively) [16].

Estimating misclassification error with small samples via bootstrap cross-validation. Bioinformatics 21, 1979–1986 (2005) 3. : A comparison of bootstrap methods and an adjusted bootstrap approach for estimating prediction error in microarray classification. Statistics in Medicine (2008) 4. : On sample size and classification accuracy: A performance comparison. S. ) ISBMDA 2005. LNCS (LNBI), vol. 3745, pp. 193–201. Springer, Heidelberg (2005) 5. : Estimating the error rate of a prediction rule: Improvement on crossvalidation.

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Artificial Neural Networks in Pattern Recognition: 4th IAPR TC3 Workshop, ANNPR 2010, Cairo, Egypt, April 11-13, 2010. Proceedings by Ahmed Al-Ani, Amir F. Atiya (auth.), Friedhelm Schwenker, Neamat El Gayar (eds.)


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