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

Show description

Read or Download A Guide to Ingres: A User's Guide to the Ingres Product PDF

Similar organization and data processing books

New PDF release: A behavioral summary for completely random nets

This paper characterizes the cycle constitution of a totally random web. Variables equivalent to variety of cycles of a distinctive size, variety of cycles, variety of cyclic states and size of cycle are studied. A sq. array of indicator variables permits conveninent examine of second constitution. also, distinct and asymptotic distributional effects are offered.

Leonard Kaufman's Finding Groups in Data: An Introduction to Cluster Analysis PDF

The Wiley-Interscience Paperback sequence comprises chosen books which were made extra obtainable to shoppers so that it will elevate worldwide allure and basic movement. With those new unabridged softcover volumes, Wiley hopes to increase the lives of those works by way of making them to be had to destiny generations of statisticians, mathematicians, and scientists.

Annual Review of Scalable Computing by Yuen Chung Kwong PDF

The 3rd within the "Series on Scalable Computing", this paintings includes 5 articles describing major advancements within the box. It addresses subject matters akin to clusters, parallel instruments, load balancing, cellular structures, and structure dependence.

Additional resources for A Guide to Ingres: A User's Guide to the Ingres Product

Sample text

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.

Download PDF sample

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

by Jeff

Rated 4.05 of 5 – based on 36 votes