-
1
-
-
33846509724
-
Minimax regret classifier for imprecise class distributions
-
Alaiz-Rodríguez, R., Guerrero-Curieses, A., Cid-Sueiro, J.: Minimax regret classifier for imprecise class distributions. Journal of Machine Learning Research 8, 103-130 (2007)
-
(2007)
Journal of Machine Learning Research
, vol.8
, pp. 103-130
-
-
Alaiz-Rodríguez, R.1
Guerrero-Curieses, A.2
Cid-Sueiro, J.3
-
4
-
-
33746064858
-
Discriminative vs. generative classifiers for cost sensitive learning
-
Lamontagne, L., Marchand, M. (eds.) Canadian AI 2006 Springer, Heidelberg
-
Drummond, C.: Discriminative vs. generative classifiers for cost sensitive learning. In: Lamontagne, L., Marchand, M. (eds.) Canadian AI 2006. LNCS (LNAI), vol.4013, pp. 481-492. Springer, Heidelberg (2006)
-
(2006)
LNCS (LNAI)
, vol.4013
, pp. 481-492
-
-
Drummond, C.1
-
5
-
-
33748991193
-
Cost curves: An improved method for visualizing classifier performance
-
Drummond, C., Holte, R.C.: Cost curves: An improved method for visualizing classifier performance. Machine Learning 65(1), 95-130 (2006)
-
(2006)
Machine Learning
, vol.65
, Issue.1
, pp. 95-130
-
-
Drummond, C.1
Holte, R.C.2
-
6
-
-
0027580356
-
Very simple classification rules perform well on most commonly used datasets
-
Holte, R.C.: Very simple classification rules perform well on most commonly used datasets. Machine Learning 11(1), 63-91 (1993)
-
(1993)
Machine Learning
, vol.11
, Issue.1
, pp. 63-91
-
-
Holte, R.C.1
-
7
-
-
84864031047
-
Correcting sample selection bias by unlabeled data
-
Schölkopf, B., Platt, J., Hoffman, T. (eds.) MIT Press, Cambridge
-
Huang, J., Smola, A.J., retton, A.G., Borgwardt, K.M., Schölkopf, B.: Correcting sample selection bias by unlabeled data. In: Schölkopf, B., Platt, J., Hoffman, T. (eds.) Advances in Neural Information Processing Systems 19, pp. 601-608. MIT Press, Cambridge (2007)
-
(2007)
Advances in Neural Information Processing Systems
, vol.19
, pp. 601-608
-
-
Huang, J.1
Smola, A.J.2
Retton, A.G.3
Borgwardt, K.M.4
Schölkopf, B.5
-
8
-
-
0035283313
-
Robust classification for imprecise environments
-
Provost, F., Fawcett, T.: Robust classification for imprecise environments. Machine Learning 42, 203-231 (2001)
-
(2001)
Machine Learning
, vol.42
, pp. 203-231
-
-
Provost, F.1
Fawcett, T.2
-
9
-
-
0002900357
-
The case against accuracy estimation for comparing induction algorithms
-
Morgan Kaufmann, San Francisco
-
Provost, F., Fawcett, T., Kohavi, R.: The case against accuracy estimation for comparing induction algorithms. In: Proceedings of the Fifteenth International Conference on Machine Learning, pp. 43-48. Morgan Kaufmann, San Francisco (1998)
-
(1998)
Proceedings of the Fifteenth International Conference on Machine Learning
, pp. 43-48
-
-
Provost, F.1
Fawcett, T.2
Kohavi, R.3
-
11
-
-
67149129014
-
-
The MIT Press, Cambridge
-
Quionero-Candela, J., Sugiyama, M., Schwaighofer, A., Lawrence, N.D.: Dataset Shift in Machine Learning. The MIT Press, Cambridge (2009)
-
(2009)
Dataset Shift in Machine Learning
-
-
Quionero-Candela, J.1
Sugiyama, M.2
Schwaighofer, A.3
Lawrence, N.D.4
-
12
-
-
84946559828
-
Exploiting context when learning to classify
-
Brazdil P.B. (ed.) ECML 1993 . Springer, Heidelberg
-
Turney, P.D.: Exploiting context when learning to classify. In: Brazdil, P.B. (ed.) ECML 1993. LNCS, vol.667, pp. 402-407. Springer, Heidelberg (1993)
-
(1993)
LNCS
, vol.667
, pp. 402-407
-
-
Turney, P.D.1
-
14
-
-
0030126609
-
Learning in the presence of concept drift and hidden contexts
-
Widmer, G., Kubat, M.: Learning in the presence of concept drift and hidden contexts. Machine Learning 23, 69-101 (1996)
-
(1996)
Machine Learning
, vol.23
, pp. 69-101
-
-
Widmer, G.1
Kubat, M.2
-
15
-
-
84858613793
-
-
Wikipedia, http://en.wikipedia.org/wiki/Post-hoc-analysis
-
Wikipedia
-
-
|