-
1
-
-
58249089342
-
-
Bennett, K.P., 1999. Combining support vector and mathematical programming methods for classification. In: Scholkopf, B., Burges, C., Smola, A., (Eds.), Advances in Kernel Methods: Support Vector Learning, MIT Press, Cambridge, MA, pp. 307-326.
-
Bennett, K.P., 1999. Combining support vector and mathematical programming methods for classification. In: Scholkopf, B., Burges, C., Smola, A., (Eds.), Advances in Kernel Methods: Support Vector Learning, MIT Press, Cambridge, MA, pp. 307-326.
-
-
-
-
2
-
-
34248555405
-
Spatial associative classification: propositional vs structural approach
-
Ceci M., and Appice A. Spatial associative classification: propositional vs structural approach. Journal of Intelligent Information Systems 27 3 (2006) 191-213
-
(2006)
Journal of Intelligent Information Systems
, vol.27
, Issue.3
, pp. 191-213
-
-
Ceci, M.1
Appice, A.2
-
3
-
-
9444271124
-
-
Ceci, M., Appice, A., Malerba, D., 2003. Mr-SBC: a multi-relational naive bayes classifier. In: Lavrac, N., Gamberger, D., Blockeel, H., Todorovski, L. (Eds.), Proceedings of the Seventh European Conference on Principles and Practice of Knowledge Discovery in Databases, PKDD 2003, Lecture Notes in Artificial Intelligence, vol. 2838. Springer, Berlin, pp. 95-106.
-
Ceci, M., Appice, A., Malerba, D., 2003. Mr-SBC: a multi-relational naive bayes classifier. In: Lavrac, N., Gamberger, D., Blockeel, H., Todorovski, L. (Eds.), Proceedings of the Seventh European Conference on Principles and Practice of Knowledge Discovery in Databases, PKDD 2003, Lecture Notes in Artificial Intelligence, vol. 2838. Springer, Berlin, pp. 95-106.
-
-
-
-
4
-
-
37249089703
-
Transductive learning from relational data
-
Perner P. (Ed), Springer, Berlin
-
Ceci M., Appice A., Barile N., and Malerba D. Transductive learning from relational data. In: Perner P. (Ed). Machine Learning and Data Mining in Pattern Recognition, MLDM, Lecture Notes in Computer Science vol. 4571 (2007), Springer, Berlin 324-338
-
(2007)
Machine Learning and Data Mining in Pattern Recognition, MLDM, Lecture Notes in Computer Science
, vol.4571
, pp. 324-338
-
-
Ceci, M.1
Appice, A.2
Barile, N.3
Malerba, D.4
-
5
-
-
58249087612
-
-
Chapelle, O., Scholkopf, B., Zien, A., 2006a. Semi-Supervised Learning. MIT Press, Cambridge, MA.
-
Chapelle, O., Scholkopf, B., Zien, A., 2006a. Semi-Supervised Learning. MIT Press, Cambridge, MA.
-
-
-
-
6
-
-
58249087048
-
-
Chapelle, O., Scholkopf, B., Zien, A., 2006b. A discussion of semi-supervised learning and transduction. In: Chapelle, O., Scholkopf, B., Zien, A. (Eds.), Semi-Supervised Learning. MIT Press, Cambridge, MA, pp. 457-462.
-
Chapelle, O., Scholkopf, B., Zien, A., 2006b. A discussion of semi-supervised learning and transduction. In: Chapelle, O., Scholkopf, B., Zien, A. (Eds.), Semi-Supervised Learning. MIT Press, Cambridge, MA, pp. 457-462.
-
-
-
-
7
-
-
0038731227
-
Learning with progressive transductive support vector machines
-
Chen Y., Wang G., and Dong S. Learning with progressive transductive support vector machines. Pattern Recognition Letters 24 (2003) 1845-1855
-
(2003)
Pattern Recognition Letters
, vol.24
, pp. 1845-1855
-
-
Chen, Y.1
Wang, G.2
Dong, S.3
-
9
-
-
84947422232
-
Attribute-value learning versus inductive logic programming: the missing links
-
Page D. (Ed), Springer, Berlin
-
De Raedt L. Attribute-value learning versus inductive logic programming: the missing links. In: Page D. (Ed). Inductive Logic Programming, 8th International Workshop, ILP 1998, Lecture Notes in Artificial Intelligence vol. 1446 (1998), Springer, Berlin 1-8
-
(1998)
Inductive Logic Programming, 8th International Workshop, ILP 1998, Lecture Notes in Artificial Intelligence
, vol.1446
, pp. 1-8
-
-
De Raedt, L.1
-
10
-
-
0031269184
-
On the optimality of the simple Bayesian classifier under zeo-ones loss
-
Domingos P., and Pazzani M. On the optimality of the simple Bayesian classifier under zeo-ones loss. Machine Learning 28 2-3 (1997) 103-130
-
(1997)
Machine Learning
, vol.28
, Issue.2-3
, pp. 103-130
-
-
Domingos, P.1
Pazzani, M.2
-
12
-
-
58249093286
-
-
Esposito, F., Malerba, D., Tamma, V., Bock, H.H., 2000. Similarity and dissimilarity. In: Bock, H.H., Diday, E. (Eds.), Analysis of Symbolic Data: Exploratory Methods for Extracting Statistical Information from Complex Data, Springer, New York, NY, pp. 139-152.
-
Esposito, F., Malerba, D., Tamma, V., Bock, H.H., 2000. Similarity and dissimilarity. In: Bock, H.H., Diday, E. (Eds.), Analysis of Symbolic Data: Exploratory Methods for Extracting Statistical Information from Complex Data, Springer, New York, NY, pp. 139-152.
-
-
-
-
13
-
-
0002947383
-
Learning by transduction
-
Morgan Kaufmann, Los Altos, CA
-
Gammerman A., Azoury K., and Vapnik V. Learning by transduction. Proceedings of the 14th Annual Conference on Uncertainty in Artificial Intelligence, UAI 1998 (1998), Morgan Kaufmann, Los Altos, CA 148-155
-
(1998)
Proceedings of the 14th Annual Conference on Uncertainty in Artificial Intelligence, UAI 1998
, pp. 148-155
-
-
Gammerman, A.1
Azoury, K.2
Vapnik, V.3
-
14
-
-
58249088189
-
-
Getoor, L., 2001. Multi-relational data mining using probabilistic relational models: research summary. In: Knobbe, A., Van der Wallen, D.M.G. (Eds.), Proceedings of the 1st Workshop in Multi-relational Data Mining, Freiburg, Germany.
-
Getoor, L., 2001. Multi-relational data mining using probabilistic relational models: research summary. In: Knobbe, A., Van der Wallen, D.M.G. (Eds.), Proceedings of the 1st Workshop in Multi-relational Data Mining, Freiburg, Germany.
-
-
-
-
15
-
-
84945286873
-
RIONA: a classifier combining rule induction and k-nn method with automated selection of optimal neighbourhood
-
Elomaa T., Mannila H., and Toivonen H. (Eds), Springer, Berlin
-
Gora G., and Wojna A. RIONA: a classifier combining rule induction and k-nn method with automated selection of optimal neighbourhood. In: Elomaa T., Mannila H., and Toivonen H. (Eds). Proceedings of the 13th European Conference on Machine Learning, ECML 2002, Lecture Notes in Artificial Intelligence vol. 2430 (2002), Springer, Berlin 111-123
-
(2002)
Proceedings of the 13th European Conference on Machine Learning, ECML 2002, Lecture Notes in Artificial Intelligence
, vol.2430
, pp. 111-123
-
-
Gora, G.1
Wojna, A.2
-
19
-
-
9444220847
-
Comparative evaluation of approaches to propositionalization
-
Horvath T., and Yamamoto A. (Eds), Springer, Berlin
-
Krogel M., Rawles S., Zelezny F., Flach P., Lavrac N., and Wrobel S. Comparative evaluation of approaches to propositionalization. In: Horvath T., and Yamamoto A. (Eds). Proceedings of the International Conference on Inductive Logic Programming, Lecture Notes in Artificial Intelligence vol. 2835 (2003), Springer, Berlin 197-214
-
(2003)
Proceedings of the International Conference on Inductive Logic Programming, Lecture Notes in Artificial Intelligence
, vol.2835
, pp. 197-214
-
-
Krogel, M.1
Rawles, S.2
Zelezny, F.3
Flach, P.4
Lavrac, N.5
Wrobel, S.6
-
20
-
-
3242775432
-
Multi-relational learning, text mining, and semi-supervised learning for functional genomics
-
Krogel M.-A., and Scheffer T. Multi-relational learning, text mining, and semi-supervised learning for functional genomics. Machine Learning 57 1-2 (2004) 61-81
-
(2004)
Machine Learning
, vol.57
, Issue.1-2
, pp. 61-81
-
-
Krogel, M.-A.1
Scheffer, T.2
-
21
-
-
84945287811
-
-
Kukar, M., Kononenko, I., 2002. Reliable classifications with machine learning. In: Elomaa, T., Mannila, H., Toivonen, H. (Eds.), Proceedings of the 13th European Conference on Machine Learning, ECML 2002. Springer, Berlin, pp. 219-231.
-
Kukar, M., Kononenko, I., 2002. Reliable classifications with machine learning. In: Elomaa, T., Mannila, H., Toivonen, H. (Eds.), Proceedings of the 13th European Conference on Machine Learning, ECML 2002. Springer, Berlin, pp. 219-231.
-
-
-
-
25
-
-
58249084646
-
-
Seeger, M., 2000. Learning with labeled and unlabeled data.
-
Seeger, M., 2000. Learning with labeled and unlabeled data.
-
-
-
-
26
-
-
58249086421
-
-
Srinivasan, A., King, R.D., Muggleton, S., 1999. The role of background knowledge: using a problem from chemistry to examine the performance of an ILP program. Technical Report PRG-TR-08-99, Oxford University Computing Laboratory.
-
Srinivasan, A., King, R.D., Muggleton, S., 1999. The role of background knowledge: using a problem from chemistry to examine the performance of an ILP program. Technical Report PRG-TR-08-99, Oxford University Computing Laboratory.
-
-
-
-
27
-
-
84880884400
-
Probabilistic classification and clustering in relational data
-
Nebel B. (Ed), Morgan Kaufmann, Los Altos, CA ISBN 1-55860-777-3
-
Taskar B., Segal E., and Koller D. Probabilistic classification and clustering in relational data. In: Nebel B. (Ed). IJCAI (2001), Morgan Kaufmann, Los Altos, CA 870-878 ISBN 1-55860-777-3
-
(2001)
IJCAI
, pp. 870-878
-
-
Taskar, B.1
Segal, E.2
Koller, D.3
-
30
-
-
58249084050
-
-
Wettschereck, D., 1994. A study of distance-based machine learning algorithms. Ph.D. Thesis, Oregon State University.
-
Wettschereck, D., 1994. A study of distance-based machine learning algorithms. Ph.D. Thesis, Oregon State University.
-
-
-
|