Bibliography - Data Mining with Decision Trees: Theory and Applications - page 290

Michalski R. S., A theory and methodology of inductive learning, Artificial
Intelligence 20:111-161, 1983.
Michalski R. S., Understanding the nature of learning: Issues and research
directions, In Machine Learning: An Artificial Intelligence Approach ,
R. Michalski, J. Carbonnel and T. Mitchell (eds.), Paolo Alto, CA:
Kaufmann, pp. 3-25, 1986.
Michalski R. S., and Tecuci G., Machine Learning, A Multistrategy Approach ,
Paolo Alto, CA: J. Morgan Kaufmann, 1994.
Michie D., Problem decomposition and the learning of skills, In Proceedings of
the European Conference on Machine Learning , pp. 17-31, Springer-Verlag,
1995.
Michie D., Spiegelhalter D. J., and Taylor C .C., Machine Learning, Neural and
Statistical Classification , Prentice Hall, 1994.
Mingers J., An empirical comparison of pruning methods for decision tree
induction, Machine Learning 4(2):227-243, 1989.
Minsky M., Logical vs. Analogical or Symbolic vs. Connectionist or Neat vs.
Scruffy, In Artificial Intelligence at MIT, Expanding Frontiers ,PatrickH.
Winston (ed.), Vol. 1, MIT Press, 1990. Reprinted in AI Magazine, 1991.
Mishra S. K., and Raghavan V. V., An empirical study of the performance of
heuristic methods for clustering, In Pattern Recognition in Practice ,E.S.
Gelsema and L. N. Kanal (eds.), 425-436, 1994.
Mitchell T., Machine Learning , McGraw-Hill, 1997.
Mitchell T., The need for biases in learning generalizations, Technical Report
CBM-TR-117, Rutgers University, Department of Computer Science, New
Brunswick, NJ, 1980.
Moody J., and Darken C., Fast learning in networks of locally tuned units, Neural
Computations 1(2):281-294, 1989.
Moore S. A., Daddario D. M., Kurinskas J. and Weiss G. M., Are decision trees
always greener on the open (source) side of the fence?, Proceedings of DMIN ,
pp. 185-188, 2009.
Morgan J. N., and Messenger R. C., THAID: A sequential search program for the
analysis of nominal scale dependent variables, Technical report, Institute
for Social Research, Univ. of Michigan, Ann Arbor, MI, 1973.
Muller W., and Wysotzki F., Automatic construction of decision trees for
classification, Annals of Operations Research 52:231-247, 1994.
Murphy O. J., and McCraw R. L., Designing storage ecient decision trees, IEEE-
TC 40(3):315-320, 1991.
Murtagh F., A survey of recent advances in hierarchical clustering algorithms
which use cluster centers, The Computer Journal 26:354-359, 1984.
Murthy S. K., Automatic construction of decision trees from data: A multi-
disciplinary survey, Data Mining and Knowledge Discovery 2(4):345-389,
1998.
Murthy S. K., Kasif S., and Salzberg S., A system for induction of oblique decision
trees, Journal of Artificial Intelligence Research 2:1-33, August 1994.