Bibliography - Data Mining with Decision Trees: Theory and Applications - page 300

Witten I. H., Frank E., and Hall M. A., Data Mining: Practical Machine Learning
Tools and Techniques , Elsevier, 2011.
Wolf L., and Shashua A., Feature selection for unsupervised and supervised
inference: The emergence of sparsity in a weight-based approach, Journal
of Machine Learning Research 6:1855-1887, 2005.
Wolpert D. H., Stacked Generalization, Neural Networks , Vol. 5, pp. 241-259,
Pergamon Press, 1992.
Wolpert D. H., The relationship between PAC, the statistical physics framework,
the Bayesian framework, and the VC framework, In The Mathematics
of Generalization, The SFI Studies in the Sciences of Complexity ,D.H.
Wolpert, (ed.), pp. 117-214, AddisonWesley, 1995.
Wolpert D. H., The lack of a priori distinctions between learning algorithms,
Neural Computation 8:1341-1390, 1996.
Woods K., Kegelmeyer W., and Bowyer K., Combination of multiple classifiers
using local accuracy estimates, IEEE Transactions on Pattern Analysis and
Machine Intelligence 19:405-410, 1997.
Wyse N., Dubes R., and Jain A. K., A critical evaluation of intrinsic dimension-
ality algorithms, In Pattern Recognition in Practice ,E.S.GelsemaandL.
N. Kanal (eds.), pp. 415-425, Amsterdam: North-Holland, 1980.
Yin W., Simmhan Y., and Prasanna V. K., Scalable regression tree learning on
hadoop using openplanet, In Proceedings of Third International Workshop
on MapReduce and its Applications Date , pp. 57-64, 2012.
Yuan Y., and Shaw M., Induction of fuzzy decision trees, Fuzzy Sets and Systems
69(1995):125-139.
Zahn C. T., Graph-theoretical methods for detecting and describing gestalt
clusters, IEEE trans. Comput. C-20(Apr.):68-86, 1971.
Zaki M. J., Ho C. T., and Agrawal R., Scalable parallel classification for data
mining on shared-memory multiprocessors, In Proc. IEEE Int. Conf. Data
Eng., Sydney, Australia , WKDD99, pp. 198-205, 1999.
Zaki M. J., and Ho C. T., (eds.), Large-Scale Parallel Data Mining ,NewYork:
Springer-Verlag, 2000.
Zantema H., and Bodlaender H. L., Finding small equivalent decision trees is hard,
International Journal of Foundations of Computer Science 11(2):343-354,
2000.
Zadrozny B., and Elkan C., Learning and making decisions when costs and
probabilities are both unknown, In Proceedings of the Seventh International
Conference on Knowledge Discovery and Data Mining (KDD'01), 2001.
Zeileis A., Hothorn T., and Hornik K., Model-based recursive partitioning,
Journal of Computational and Graphical Statistics 17(2):492-514, 2008.
Zeira G., Maimon O., Last M., and Rokach L., Change Detection in Classification
Models of Data Mining, Data Mining in Time Series Databases , Singapore:
World Scientific Publishing, 2003.
Zenobi G., and Cunningham P. Using diversity in preparing ensembles of classi-
fiers based on different feature subsets to minimize generalization error, In
Proceedings of the European Conference on Machine Learning , 2001.