A Guide to Ingres: A User's Guide to the Ingres Product by C. J. Date PDF

By C. J. Date

ISBN-10: 020106006X

ISBN-13: 9780201060065

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A-\-b + c-{-d (6) ^^ This measure is also named "double-fault measure" because it is the proportion of the examples that have been misclassified by both classifiers. Partridge and Yates (1996), and Margineantu and Diettrich (1997) have used a measure named within-set generalization diversity. This measure is simply the K statistics developed by Fleiss (1981) as a measure of interrater reliability. It measures the level of agreement between two classifiers with the correction for chance. The pairwise version of the K is calculated as: 2(ac - bd) {a + b){c + d)-\-{a + c){b 4- d)' (7) Skalak (1996) has used the disagreement measure to characterize the diversity between base classifiers: DMij = b+ c -— -.

And BAKIRI, G. (1995): Solving multiclass learning problem via error-correcting output codes. Journal of Artificial Intelligence Research, 2, 263-286. L. (1981): Statistical Methods for Rates and Proportions. John Wiley and Sons, New York. FREUND, Y. E. (1997): A decision-theoretic generalization of on-line learning and an appfication to boosting. Journal of Computer and System Sciences 55, 119-139. GIACINTO, G. and ROLI, F. (2001): Design of effective neural network ensembles for image classification processes.

Department of Information and Computer Science, University of California, Irvine. BREIMAN, L. (1996): Bagging predictors. Machine Learning, 24, 123-140. BREIMAN, L. (1998): Arcing classifiers. Annals of Statistics, 26, 801-849. BREIMAN, L. (2001): Random Forests. Machine Learning 45, 5-32. CUNNIGHAM, R and CARNEY, J. (2000): Diversity versus quality in classification ensembles based on feature selection. In: Proceedings of European Conference on Machine Learning^ LNCS, vol. 1810, Springer, Berlin, 109-116.

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A Guide to Ingres: A User's Guide to the Ingres Product by C. J. Date


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