Bibliography - Data Mining with Decision Trees: Theory and Applications - page 286

Kohavi R., and Kunz C., Option decision trees with majority votes, In Machine
Learning: Proceedings of the Fourteenth International Conference ,D.Fisher
(ed.), pp. 161-169, Morgan Kaufmann Publishers, Inc., 1997.
Kohavi R., and Provost F., Glossary of Terms, Machine Learning 30(2/3):271-
274, 1998.
Kohavi R., and Quinlan J. R., Decision-tree discovery, In Handbook of Data
Mining and Knowledge Discovery ,W.KlosgenandJ.M.Zytkow(eds.),
Chapter 16.1.3, pp. 267-276, Oxford: Oxford University Press, 2002.
Kohavi R., and Sommerfield D., Targeting business users with decision table
classifiers, In Proceedings of the Fourth International Conference on Knowl-
edge Discovery and Data Mining , R. Agrawal, P. Stolorz and G. Piatetsky-
Shapiro (eds.), pp. 249-253, Portland, OR: AAAI Press, 1998.
Kohavi R., and Wolpert D. H., Bias plus variance decomposition for zero-one
loss functions, Machine Learning: Proceedings of the 13th International
Conference , Morgan Kaufman, 1996.
Kolcz A., Chowdhury A., and Alspector J., Data duplication: An imbalance prob-
lem, In Workshop on Learning from Imbalanced Data Sets (ICML), 2003.
Kolen J. F., and Pollack J. B., Back propagation is sesitive to initial conditions,
In Advances in Neural Information Processing Systems , Vol. 3, pp. 860-867,
San Francisco, CA: Morgan Kaufmann, 1991.
Koller D., and Sahami M., Towards optimal feature selection, In Machine Learn-
ing: Proceedings of the Thirteenth International Conference on machine
Learning , Morgan Kaufmann, 1996.
Kononenko I., Comparison of inductive and Naive Bayes learning approaches to
automatic knowledge acquisition, In Current Trends in Knowledge Acqui-
sition , B. Wielinga (ed.), Amsterdam, The Netherlands: IOS Press, 1990.
Kononenko I., SemiNaive Bayes classifier, In Proceedings of the Sixth European
Working Session on Learning , pp. 206-219, Porto, Portugal: SpringerVer-
lag, 1991.
Krtowski M., An evolutionary algorithm for oblique decision tree induction, In
Proc. of ICAISC'04 , Springer, LNCS 3070, pp. 432-437, 2004.
Krtowski M., and Grze M., Global learning of decision trees by an evolutionary
algorithm, In Information Processing and Security Systems , K. Saeed and
J. Peja (eds.), Springer, pp. 401-410, 2005.
Krogh A., and Vedelsby J., Neural network ensembles, cross validation and active
learning, Advances in Neural Information Processing Systems 7:231-238,
1995.
Kuhn H. W., The Hungarian method for the assignment problem, Naval Research
Logistics Quarterly 2:83-97, 1955.
Kuncheva L. I., Diversity in multiple classifier systems (Editorial), Information
Fusion 6(1):3-4, 2005.
Kuncheva L., and Whitaker C., Measures of diversity in classifier ensembles
and their relationship with ensemble accuracy, Machine Learning : 181-207,
2003.
Kusiak A., Decomposition in data mining: An industrial case study, IEEE
Transactions on Electronics Packaging Manufacturing 23(4):345-353, 2000.