Biomedical Engineering Reference
In-Depth Information
[28] A. Dempster, N. Laird, and D. Rubin, ''Maximum-likelihood from incomplete data via
the em algorithm (with discussion),''
Journal of the Royal Statistical Society, Series B
,
vol. 39, pp. 1--38, 1977.
[29] D. MacKay, ''Bayesian model comparison and backprop nets,'' in
Neural Networks for
Signal Processing 4
(J. Moody, S. Hanson, and R. Lippmann, eds.), (San Mateo, CA),
pp. 839--846, Morgan Kaufmann, 1992.
[30] A. Barron and T. Cover, ''Minimum complexity density estimation,''
IEEE Transactions
on information theory
, vol. IT-37, no. 4, pp. 1034--1054, 1991.
[31] A. Same, C. Ambroise, and G. Govaert, ''An online classification em algorithm based on
the mixture model,''
Statistics and Computing
, vol. 17, no. 3, pp. 209--218, 2007.
[32] E. Parzen, ''On estimation of a probability density function and mode,''
Annals of math-
ematical statistics
, vol. 33, no. 3, pp. 1065--1076, 1962.
[33] H. Zhang, ''The optimality of naive bayes,''
Proceedings of Florida Artificial Intelligence
Research Society (FLAIRS)
, 2004.
[34] J. L. Bentley, ''Multidimensional binary search trees used for associative searching,''
Com-
mun. ACM
, vol. 18, no. 9, pp. 509--517, 1975.
[35] A. Beygelzimer, S. Kakade, and J. Langford, ''Cover trees for nearest neighbor,''
ICML
'06: Proceedings of the 23rd International Conference on Machine Learning
, (New York,
NY), pp. 97--104, ACM, 2006.
[36] L. Breiman, J. Friedman, R. Olshen, and C. Stone,
Classification and Regression Trees
,
New York: Chapman and Hall, 1993.
[37] J. Quinlan, ''Learning efficient classification procedures and their application to chess end
games,'' in
Machine Learning: An Artificial Intelligence Approach
(R. Michalski, J. Car-
bonell, and T. Mitchell, eds.), (San Francisco, CA), pp. 463--482, Morgan Kaufmann,
1983.
[38] J. Quinlan,
C4.5: Programs for Machine Learning
, San Francisco: Morgan Kaufmann,
1993.
[39] M. Mehta, J. Rissanen, and R. Agrawal, ''Mdl-based decision tree pruning,''
Proceed-
ings of the First International Conference on Knowledge Discovery and Data Mining
,
pp. 216--221, 1995.
[40] J. C. Shafer, R. Agrawal, and M. Mehta, ''Sprint: A scalable parallel classifier for data
mining,''
VLDB'96, Proceedings of 22th International Conference on Very Large Data
Bases, September 3-6, 1996, Mumbai (Bombay), India
(T. M. Vijayaraman, A. P. Buch-
mann, C. Mohan, and N. L. Sarda, eds.), pp. 544--555, Morgan Kaufmann, 1996.
[41] M. Mehta, R. Agrawal, and J. Rissanen, ''Sliq: A fast scalable classifier for data mining,''
EDBT '96: Proceedings of the 5th International Conference on Extending Database Tech-
nology
, (London, UK), pp. 18--32, Springer-Verlag, 1996.
[42] E.-H. Han, G. Karypis, and V. Kumar, ''Scalable parallel data mining for association
rules,''
SIGMOD '97: Proceedings of the 1997 ACM SIGMOD International Conference
on Management of Data
, (New York, NY), pp. 277--288, ACM, 1997.
[43] N. Amado, J. Gama, and F. M. A. Silva, ''Parallel implementation of decision tree learning
algorithms,''
EPIA '01: Proceedings of the 10th Portuguese Conference on Artificial Intel-
ligence on Progress in Artificial Intelligence, Knowledge Extraction, Multi-agent Systems,
Logic Programming and Constraint Solving
, (London, UK), pp. 6--13, Springer-Verlag,
2001.
[44]
S. Chunguang, ''Parallel solution of sparse linear least squares problems on distributed-
memory multiprocessors,''
Parallel Computing
, vol. 23, no. 13, pp. 2075--2093, 1997.
[45]
A. Turing, ''Intelligent machinery,'' in
Collected Works of A.M. Turing: Mechanical Intel-
ligence
(D. Ince, ed.), (Amsterdam, The Netherlands), Elsevier Science Publishers, 1992.
[46]
R. Duda, P. Hart, and D. Stork,
Pattern Classification
, New York: John Wiley and Sons,
Inc., 2001.
Search WWH ::
Custom Search