-
2
-
-
33746327471
-
-
S.G. Bottcher, Learning Bayesian networks with mixed variables, PhD thesis, Aalborg University, 2004.
-
-
-
-
4
-
-
33746379591
-
-
J. Cheng, R. Greiner, Comparing Bayesian network classifiers, in: Proceedings of the 15th Conference on Uncertainty in Artificial Intelligence, 1999, pp. 101-107.
-
-
-
-
5
-
-
84933530882
-
Approximating discrete probability distributions with dependence trees
-
Chow C., and Liu C. Approximating discrete probability distributions with dependence trees. IEEE Transactions on Information Theory 14 (1968) 462-467
-
(1968)
IEEE Transactions on Information Theory
, vol.14
, pp. 462-467
-
-
Chow, C.1
Liu, C.2
-
10
-
-
0031269184
-
On the optimality of the simple Bayesian classifier under zero-one loss
-
Domingos P., and Pazzani M. On the optimality of the simple Bayesian classifier under zero-one loss. Machine Learning 29 (1997) 103-130
-
(1997)
Machine Learning
, vol.29
, pp. 103-130
-
-
Domingos, P.1
Pazzani, M.2
-
13
-
-
35048903577
-
-
M. Egmont-Peterson, Feature selection by Markov chain Monte Carlo sampling: a Bayesian approach, in: Proceedings of the Joint IAPR Workshops SSPR 2004 and SPR 2004, 2004, pp. 1034-1042.
-
-
-
-
14
-
-
33746379280
-
-
U. Fayyad, K. Irani, Multi-interval discretization of continuous-valued attributes for classification learning, in: Proceedings of the 13th International Conference on Artificial Intelligence, 1993, pp. 1022-1027.
-
-
-
-
15
-
-
0000764772
-
The use of multiple measurements
-
Fisher R.A. The use of multiple measurements. Annals of Eugenics 7 (1936) 179-188
-
(1936)
Annals of Eugenics
, vol.7
, pp. 179-188
-
-
Fisher, R.A.1
-
16
-
-
21744462998
-
On bias, variance, 0/1 - loss, and the curse-of-dimensionality
-
Friedman J.H. On bias, variance, 0/1 - loss, and the curse-of-dimensionality. Data Mining and Knowledge Discovery 1 (1997) 55-77
-
(1997)
Data Mining and Knowledge Discovery
, vol.1
, pp. 55-77
-
-
Friedman, J.H.1
-
18
-
-
33746353403
-
-
N. Friedman, M. Goldszmidt, T. Lee, Bayesian network classification with continuous attributes: getting the best of both discretization and parametric fitting, in: Proceedings of the 15th National Conference on Machine Learning, 1998.
-
-
-
-
19
-
-
33746366814
-
-
D. Geiger, D. Heckerman, Learning Gaussian networks, Technical Report, Microsoft Research, Advanced Technology Division, 1994.
-
-
-
-
20
-
-
0030125397
-
Beyond Bayesian networks: similarity networks and Bayesian multinets
-
Geiger D., and Heckerman D. Beyond Bayesian networks: similarity networks and Bayesian multinets. Artificial Intelligence 82 (1996) 45-74
-
(1996)
Artificial Intelligence
, vol.82
, pp. 45-74
-
-
Geiger, D.1
Heckerman, D.2
-
22
-
-
0001099335
-
Decomposable graphical Gaussian model determination
-
Giudici P., and Green P.J. Decomposable graphical Gaussian model determination. Biometrika 86 4 (1999) 785-801
-
(1999)
Biometrika
, vol.86
, Issue.4
, pp. 785-801
-
-
Giudici, P.1
Green, P.J.2
-
24
-
-
14344256569
-
-
D. Grossman, P. Domingos, Learning Bayesian network classifiers by maximizing conditional likelihood, in: Proceeding of the 21th International Conference on Machine Learning, 2004.
-
-
-
-
26
-
-
33746343337
-
-
M.A. Hall, L.A. Smith, Feature subset selection: a correlation based filter approach, in: Proceeding of the Fourth International Conference on Neural Information Processing and Intelligent Information Systems, 1997, pp. 855-858.
-
-
-
-
27
-
-
34249761849
-
Learning Bayesian networks: the combination of knowledge and statistical data
-
Heckerman D.E., Geiger D., and Chickering D. Learning Bayesian networks: the combination of knowledge and statistical data. Machine Learning 20 (1995) 197-243
-
(1995)
Machine Learning
, vol.20
, pp. 197-243
-
-
Heckerman, D.E.1
Geiger, D.2
Chickering, D.3
-
28
-
-
0037403462
-
Variance and bias for general loss functions
-
James G.M. Variance and bias for general loss functions. Machine Learning 51 (2003) 115-135
-
(2003)
Machine Learning
, vol.51
, pp. 115-135
-
-
James, G.M.1
-
29
-
-
33746365566
-
-
T. Jebara, Discriminative, generative, and imitative learning, PhD thesis, Massachusetts Institute of Technology, 2001.
-
-
-
-
30
-
-
33746331059
-
-
G. John, P. Langley, Estimating continuous distributions in Bayesian classifiers, in: Proceedings of the 11th Conference on Uncertainty in Artificial Intelligence, 1995, pp. 338-345.
-
-
-
-
33
-
-
33746361205
-
-
E.J. Keogh, M. Pazzani, Learning augmented Bayesian classifiers: a comparison of distribution-based and non distribution-based approaches, in: Proceedings of the 7th International Workshop on Artificial Intelligence and Statistics, 1999, pp. 225-230.
-
-
-
-
34
-
-
33746333295
-
-
R. Kohavi, Wrappers for performance enhancement and oblivious decision graphs, PhD Thesis, Computer Science department, 1995.
-
-
-
-
35
-
-
0031381525
-
Wrappers for feature subset selection
-
Kohavi R., and John G. Wrappers for feature subset selection. Artificial Intelligence 97 1-2 (1997) 273-324
-
(1997)
Artificial Intelligence
, vol.97
, Issue.1-2
, pp. 273-324
-
-
Kohavi, R.1
John, G.2
-
36
-
-
33746357585
-
-
R. Kohavi, D.H. Wolpert, Bias plus variance decomposition for zero-one loss functions, in: International Conference on Machine Learning, 1996.
-
-
-
-
37
-
-
85031799549
-
-
I. Kononenko, Semi-naive Bayesian classifiers, in: Proceedings of the 6th European Working Session on Learning, 1991, pp. 206-219.
-
-
-
-
38
-
-
0033640901
-
Comparison of algorithms that select features for pattern classifiers
-
Kudo M. Comparison of algorithms that select features for pattern classifiers. Machine Learning 33 1 (2000) 25-41
-
(2000)
Machine Learning
, vol.33
, Issue.1
, pp. 25-41
-
-
Kudo, M.1
-
39
-
-
0026992322
-
-
P. Langley, W. Iba, K. Thompson, An analysis of Bayesian classifiers, in: Proceedings of the 10th National Conference on Artificial Intelligence, 1992, pp. 223-228.
-
-
-
-
40
-
-
33746327470
-
-
P. Langley, S. Sage, Induction of selective Bayesian classifiers, in: Proceedings of the 10th Conference on Uncertainty in Artificial Intelligence, 1994, pp. 399-406.
-
-
-
-
43
-
-
33746324249
-
-
S.L. Lauritzen, N. Wermuth, Mixed interaction models. Technical Report r 84-8, Institute for Electronic Systems, Aalborg University, 1984.
-
-
-
-
44
-
-
0002480085
-
Graphical models for associations between variables, some of which are qualitative and some quantitative
-
Lauritzen S.L., and Wermuth N. Graphical models for associations between variables, some of which are qualitative and some quantitative. Annals of Statistics 17 (1989)
-
(1989)
Annals of Statistics
, vol.17
-
-
Lauritzen, S.L.1
Wermuth, N.2
-
47
-
-
33746333867
-
-
P.M. Murphy, D.W. Aha, UCI repository of machine learning databases, Technical Report, University of California at Irvine, 1995. Available from: .
-
-
-
-
49
-
-
33746331397
-
-
M. Pazzani, Searching for dependencies in Bayesian classifiers, in: Learning from Data: Artificial Intelligence and Statistics V, 1997, pp. 239-248.
-
-
-
-
51
-
-
4644329616
-
Bayesian network classifier versus k-NN classifier
-
Pernkopf F. Bayesian network classifier versus k-NN classifier. Pattern Recognition 38 1 (2005) 1-10
-
(2005)
Pattern Recognition
, vol.38
, Issue.1
, pp. 1-10
-
-
Pernkopf, F.1
-
52
-
-
31844434495
-
-
F. Pernkopf, J. Bilmes, Discriminative versus generative parameter and structure learning of Bayesian network classifiers, in: Proceedings of the 22nd International Conference in Machine Learning, 2005.
-
-
-
-
53
-
-
33744584654
-
Induction of decision trees
-
Quinlan J.R. Induction of decision trees. Machine Learning 1 (1986) 81-106
-
(1986)
Machine Learning
, vol.1
, pp. 81-106
-
-
Quinlan, J.R.1
-
55
-
-
33746351492
-
-
R. Raina, Y. Shen, A.Y. Ng, A. McCallum, Classification with hybrid generative/discriminative models, in: Advances in Neural Information Processing Systems 16, 2003.
-
-
-
-
57
-
-
33746339228
-
-
M. Sahami, Learning limited dependence Bayesian classifiers, in: Proceedings of the 2nd International Conference on Knowledge Discovery and Data Mining, 1996, pp. 335-338.
-
-
-
-
58
-
-
26944496426
-
-
G. Santaf́e, J.A. Lozano, P. Larrañaga, Discriminative learning of Bayesian network classifiers via the TM algorithm, in: Proceedings of the Eighth European Conference on Symbolic and Quantitative Approaches to Reasoning with Uncertainty, 2005, pp. 148-160.
-
-
-
-
59
-
-
3242765279
-
A bias-variance analysis of a real world learning problem: the CoIL challenge 2000
-
van der Putten P., and van Someren M. A bias-variance analysis of a real world learning problem: the CoIL challenge 2000. Machine Learning 57 (2004) 177-195
-
(2004)
Machine Learning
, vol.57
, pp. 177-195
-
-
van der Putten, P.1
van Someren, M.2
-
60
-
-
33746326483
-
-
H. Wang, Towards a unified framework of relevance, PhD Thesis, Faculty of Informatics, University of Ulster, 1996.
-
-
-
-
63
-
-
33746368383
-
-
Y. Yang, G.I. Webb, Discretization for naive-Bayes learning: managing discretization bias and variance, Technical Report 2003-131, School of Computer Science and Software Engineering, Monash University, 2003.
-
-
-
-
64
-
-
25144492516
-
Efficient feature selection via analysis of relevance and redundancy
-
Yu L., and Liu H. Efficient feature selection via analysis of relevance and redundancy. Machine Learning Research 5 (2004) 1205-1224
-
(2004)
Machine Learning Research
, vol.5
, pp. 1205-1224
-
-
Yu, L.1
Liu, H.2
|