Databases Reference
In-Depth Information
[MFS95]
D. Malerba, E. Floriana, and G. Semeraro. A further comparison of simplification meth-
ods for decision tree induction. In D. Fisher and H. Lenz (eds.),
Learning from Data: AI
and Statistics
. Springer Verlag, 1995.
[MH95]
J. K. Martin and D. S. Hirschberg. The time complexity of decision tree induction. In
Technical Report ICS-TR 95-27
, pp. 1-27, Department of Information and Computer
Science, University of California, Irvine, CA, Aug. 1995.
[MH09]
H. Miller and J. Han.
Geographic Data Mining and Knowledge Discovery
(2nd ed.).
Chapman & Hall/CRC, 2009.
[Mic83]
R. S. Michalski. A theory and methodology of inductive learning. In R. S. Michalski,
J. G. Carbonell, and T. M. Mitchell (eds.),
Machine Learning: An Artificial Intelligence
Approach
, Vol. 1, pp. 83-134. Morgan Kaufmann, 1983.
[Mic92]
Z. Michalewicz.
Genetic Algorithms
C
Data Structures
D
Evolution Programs
. Springer
Verlag, 1992.
[Mil98]
R. G. Miller.
Survival Analysis
. Wiley-Interscience, 1998.
[Min89]
J. Mingers. An empirical comparison of pruning methods for decision-tree induction.
Machine Learning
, 4:227-243, 1989.
[Mir98]
B. Mirkin. Mathematical classification and clustering.
J. Global Optimization
, 12:105-
108, 1998.
[Mit96]
M. Mitchell.
An Introduction to Genetic Algorithms
. Cambridge, MA: MIT Press, 1996.
[Mit97]
T. M. Mitchell.
Machine Learning
. McGraw-Hill, 1997.
[MK91]
M. Manago and Y. Kodratoff. Induction of decision trees from complex structured data.
In G. Piatetsky-Shapiro and W. J. Frawley (eds.),
Knowledge Discovery in Databases
,
pp. 289-306. AAAI/MIT Press, 1991.
[MLSZ06]
Q. Mei, C. Liu, H. Su, and C. Zhai. A probabilistic approach to spatiotemporal theme
pattern mining on weblogs. In
Proc. 15th Int. Conf. World Wide Web (WWW'06)
,
pp. 533-542, Edinburgh, Scotland, May 2006.
[MM95]
J. Major and J. Mangano. Selecting among rules induced from a hurricane database.
J. Intelligent Information Systems
, 4:39-52, 1995.
[MM02]
G. Manku and R. Motwani. Approximate frequency counts over data streams. In
Proc.
2002 Int. Conf. Very Large Data Bases (VLDB'02)
, pp. 346-357, Hong Kong, China, Aug.
2002.
[MN89]
M. Mezard and J.-P. Nadal. Learning in feedforward layered networks: The tiling
algorithm.
J. Physics
, 22:2191-2204, 1989.
[MO04]
S. C. Madeira and A. L. Oliveira. Biclustering algorithms for biological data analysis: A
survey.
IEEE/ACM Trans. Computational Biology and Bioinformatics
, 1(1):24-25, 2004.
[MP69]
M. L. Minsky and S. Papert.
Perceptrons: An Introduction to Computational Geometry
.
Cambridge, MA: MIT Press, 1969.
[MRA95]
M. Metha, J. Rissanen, and R. Agrawal. MDL-based decision tree pruning. In
Proc.
1995 Int. Conf. Knowledge Discovery and Data Mining (KDD'95)
, pp. 216-221, Montreal,
Quebec, Canada, Aug. 1995.
[MRS08]
C. D. Manning, P. Raghavan, and H. Schutze.
Introduction to Information Retrieval
.
Cambridge University Press, 2008.
[MS03a]
M. Markou and S. Singh. Novelty detection: A review—part 1: Statistical approaches.
Signal Processing
, 83:2481-2497, 2003.