-
1
-
-
84898982358
-
Co-validation: Using model disagreement to validate classification algorithms
-
Madani, O., Pennock, D.M., Flake, G.W.: Co-validation: Using model disagreement to validate classification algorithms. In: NIPS 2004 Preproceedings. (2004)
-
(2004)
NIPS 2004 Preproceedings
-
-
Madani, O.1
Pennock, D.M.2
Flake, G.W.3
-
4
-
-
84947134568
-
Learning from a mixture of labeled and unlabeled examples with parametric side information
-
New York, NY, USA, ACM Press
-
Ratsaby, J., Venkatesh, S.S.: Learning from a mixture of labeled and unlabeled examples with parametric side information. In: Proceedings of the 8th Annual Conference on Computational Learning Theory (COLT'95), New York, NY, USA, ACM Press (1995) 412-417
-
(1995)
Proceedings of the 8th Annual Conference on Computational Learning Theory (COLT'95)
, pp. 412-417
-
-
Ratsaby, J.1
Venkatesh, S.S.2
-
6
-
-
0036643079
-
Metric-based methods for adaptive model selection and regularization
-
Schuurmans, D., Southey, F.: Metric-based methods for adaptive model selection and regularization. Machine Learning 42 (2002) 51-84
-
(2002)
Machine Learning
, vol.42
, pp. 51-84
-
-
Schuurmans, D.1
Southey, F.2
-
8
-
-
0013361426
-
Learning by distances
-
Ben-David, S., Itai, A., Kushilevitz, E.: Learning by distances. Information and Computation 117 (1995) 240-250
-
(1995)
Information and Computation
, vol.117
, pp. 240-250
-
-
Ben-David, S.1
Itai, A.2
Kushilevitz, E.3
-
9
-
-
0038453192
-
Rademacher and Gaussian complexities: Risk bounds and structural results
-
Bartlett, P.L., Mendelson, S.: Rademacher and Gaussian complexities: Risk bounds and structural results. Journal of Machine Learning Research 3 (2002) 463-482
-
(2002)
Journal of Machine Learning Research
, vol.3
, pp. 463-482
-
-
Bartlett, P.L.1
Mendelson, S.2
-
10
-
-
26944441121
-
Relating the Rademacher and VC bounds
-
Department of Computer Science, Series of Publications C
-
Kääriäinen, M.: Relating the Rademacher and VC bounds. Technical Report Report C-2004-57, Department of Computer Science, Series of Publications C (2004)
-
(2004)
Technical Report Report
, vol.C-2004-57
-
-
Kääriäinen, M.1
-
11
-
-
0021518106
-
A theory of the learnable
-
Valiant, L.G.: A theory of the learnable. Communications of the ACM 27 (1984) 1134-1142
-
(1984)
Communications of the ACM
, vol.27
, pp. 1134-1142
-
-
Valiant, L.G.1
-
12
-
-
0037399538
-
PAC-Bayesian stochastic model selection
-
McAllester, D.A.: PAC-Bayesian stochastic model selection. Machine Learning 51 (2003) 5-21
-
(2003)
Machine Learning
, vol.51
, pp. 5-21
-
-
McAllester, D.A.1
-
14
-
-
0033280893
-
Beating the hold-out: Bounds for k-fold and progressive cross-validation
-
New York, NY, ACM Press
-
Blum, A., Kalai, A., Langford, J.: Beating the hold-out: bounds for k-fold and progressive cross-validation. In: Proceedings of the 12th Annual Conference on Computational Learning Theory, New York, NY, ACM Press (1999) 203-208
-
(1999)
Proceedings of the 12th Annual Conference on Computational Learning Theory
, pp. 203-208
-
-
Blum, A.1
Kalai, A.2
Langford, J.3
-
15
-
-
0003336572
-
A probabilistic theory of pattern recognition
-
Springer, Berlin Heidelberg New York
-
Devroye, L., Györfi, L., Lugosi, G.: A Probabilistic Theory of Pattern Recognition. Volume 31 of Applications of Mathematics. Springer, Berlin Heidelberg New York (1996)
-
(1996)
Applications of Mathematics
, vol.31
-
-
Devroye, L.1
Györfi, L.2
Lugosi, G.3
-
16
-
-
84880904019
-
Practical prediction theory for classification
-
A tutorial presented
-
Langford, J.: Practical prediction theory for classification (2003) A tutorial presented at ICML 2003. Available at http://hunch.net/~jl/projects/ prediction_bounds/tutorial/tutorial.pdf.
-
(2003)
ICML 2003
-
-
Langford, J.1
-
17
-
-
3142674150
-
Almost-everywhere algorithmic stability and generalization error
-
Kutin, S., Niyogi, P.: Almost-everywhere algorithmic stability and generalization error. In: Proceedings of Uncertainty in AI. (2002) 275-282
-
(2002)
Proceedings of Uncertainty in AI
, pp. 275-282
-
-
Kutin, S.1
Niyogi, P.2
-
18
-
-
0002714543
-
Making large-scale SVM learning practical
-
Schölkopf, B., Burges, C., Smola, A., eds.: MIT-Press
-
Joachims, T.: Making large-scale SVM learning practical. In Schölkopf, B., Burges, C., Smola, A., eds.: Advances in Kernel Methods - Support Vector Learning. MIT-Press (1999)
-
(1999)
Advances in Kernel Methods - Support Vector Learning
-
-
Joachims, T.1
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