-
1
-
-
84880688943
-
Learning probabilistic relational models
-
N. Friedman, L. Getoor, D. Koller, and A. Pfeffer, "Learning probabilistic relational models," in IJCAI, 1999.
-
(1999)
IJCAI
-
-
Friedman, N.1
Getoor, L.2
Koller, D.3
Pfeffer, A.4
-
2
-
-
1942418618
-
Discriminative probabilistic models for relational data
-
B. Taskar, P. Abbeel, and D. Koller, "Discriminative probabilistic models for relational data," in UAI, 2002.
-
(2002)
UAI
-
-
Taskar, B.1
Abbeel, P.2
Koller, D.3
-
3
-
-
32044466073
-
Markov logic networks
-
M. Richardson and P. Domingos, "Markov logic networks," Mach. Learn., vol. 62, no. 1-2, pp. 107-136, 2006.
-
(2006)
Mach. Learn.
, vol.62
, Issue.1-2
, pp. 107-136
-
-
Richardson, M.1
Domingos, P.2
-
4
-
-
33947664999
-
Relational dependency networks
-
J. Neville and D. Jensen, "Relational dependency networks," J. Mach. Learn. Res., vol. 8, pp. 653-692, 2007.
-
(2007)
J. Mach. Learn. Res.
, vol.8
, pp. 653-692
-
-
Neville, J.1
Jensen, D.2
-
5
-
-
38049174896
-
Efficient weight learning for markov logic networks
-
D. Lowd and P. Domingos, "Efficient weight learning for markov logic networks," in PKDD, 2007, pp. 200-211.
-
(2007)
PKDD
, pp. 200-211
-
-
Lowd, D.1
Domingos, P.2
-
6
-
-
84880885580
-
Training conditional random fields using virtual evidence boosting
-
L. Liao, T. Choudhury, D. Fox, and H. Kautz, "Training conditional random fields using virtual evidence boosting," in IJCAI, 2007.
-
(2007)
IJCAI
-
-
Liao, L.1
Choudhury, T.2
Fox, D.3
Kautz, H.4
-
8
-
-
34249102504
-
Classification in networked data: A toolkit and a univariate case study
-
no. May
-
S. Macskassy and F. Provost, "Classification in networked data: A toolkit and a univariate case study," Journal of Machine Learning Research, vol. 8, no. May, pp. 935-983, 2007.
-
(2007)
Journal of Machine Learning Research
, vol.8
, pp. 935-983
-
-
MacSkassy, S.1
Provost, F.2
-
9
-
-
75249087898
-
Cautious collective classification
-
L. McDowell, K. Gupta, and D. Aha, "Cautious collective classification," Journal of Machine Learning Research, vol. 10, pp. 2777-2836, 2009.
-
(2009)
Journal of Machine Learning Research
, vol.10
, pp. 2777-2836
-
-
McDowell, L.1
Gupta, K.2
Aha, D.3
-
10
-
-
0001235413
-
The description of a random field by means of conditional probabilities and conditions of its regularity
-
P. L. Dobrushin, "The description of a random field by means of conditional probabilities and conditions of its regularity," Theory Probab. Appl., vol. 13, pp. 197-224, 1968.
-
(1968)
Theory Probab. Appl.
, vol.13
, pp. 197-224
-
-
Dobrushin, P.L.1
-
11
-
-
53749083869
-
Collective classification in network data
-
P. Sen, G. Namata, M. Bilgic, L. Getoor, B. Galligher, and T. Eliassi-Rad, "Collective classification in network data," Ai Magazine, vol. 29, no. 3, 2008.
-
(2008)
Ai Magazine
, vol.29
, Issue.3
-
-
Sen, P.1
Namata, G.2
Bilgic, M.3
Getoor, L.4
Galligher, B.5
Eliassi-Rad, T.6
-
12
-
-
1942450651
-
Linkage and autocorrelation cause feature selection bias in relational learning
-
D. Jensen and J. Neville, "Linkage and autocorrelation cause feature selection bias in relational learning," in ICML, 2002.
-
(2002)
ICML
-
-
Jensen, D.1
Neville, J.2
-
13
-
-
67049098006
-
Why stacked models perform effective collective classification
-
A. Fast and D. Jensen, "Why stacked models perform effective collective classification," in ICDM, 2008.
-
(2008)
ICDM
-
-
Fast, A.1
Jensen, D.2
-
14
-
-
34249042076
-
Leveraging relational autocorrelation with latent group models
-
J. Neville and D. Jensen, "Leveraging relational autocorrelation with latent group models," in ICDM, 2005.
-
(2005)
ICDM
-
-
Neville, J.1
Jensen, D.2
-
15
-
-
33846313242
-
Introduction to statistical learning theory
-
Springer
-
O. Bousquet, S. Boucheron, and G. Lugosi, "Introduction to statistical learning theory," in In, O. Bousquet, U.v. Luxburg, and G. Rsch (Editors). Springer, 2004, pp. 169-207.
-
(2004)
O. Bousquet, U.v. Luxburg, and G. Rsch (Editors)
, pp. 169-207
-
-
Bousquet, O.1
Boucheron, S.2
Lugosi, G.3
-
16
-
-
0003161174
-
Rates of convergence for empirical processes of stationary mixing sequences
-
B. Yu, "Rates of convergence for empirical processes of stationary mixing sequences," Ann. Probab., vol. 22, no. 1, 1994.
-
(1994)
Ann. Probab.
, vol.22
, Issue.1
-
-
Yu, B.1
-
17
-
-
84877763981
-
Relational learning with one network: An asymptotic analysis
-
R. Xiang and J. Neville, "Relational learning with one network: An asymptotic analysis," in AISTATS, 2011.
-
(2011)
AISTATS
-
-
Xiang, R.1
Neville, J.2
-
18
-
-
50649124443
-
A bias/variance decomposition for models using collective inference
-
J. Neville and D. Jensen, "A bias/variance decomposition for models using collective inference," Machine Learning, vol. 73, pp. 87-106, 2008.
-
(2008)
Machine Learning
, vol.73
, pp. 87-106
-
-
Neville, J.1
Jensen, D.2
-
20
-
-
65449149851
-
Effective label acquisition for collective classification
-
M. Bilgic and L. Getoor, "Effective label acquisition for collective classification," in KDD, 2008.
-
(2008)
KDD
-
-
Bilgic, M.1
Getoor, L.2
-
21
-
-
34547984927
-
Learning to extract symbolic knowledge from the World Wide Web
-
M. Craven, D. DiPasquo, D. Freitag, A. McCallum, T. Mitchell, K. Nigam, and S. Slattery, "Learning to extract symbolic knowledge from the World Wide Web," in AAAI, 1998.
-
(1998)
AAAI
-
-
Craven, M.1
Dipasquo, D.2
Freitag, D.3
McCallum, A.4
Mitchell, T.5
Nigam, K.6
Slattery, S.7
-
22
-
-
70350635917
-
Using graph-based metrics with empirical risk minimization to speed up active learning on networked data
-
S. A. Macskassy, "Using graph-based metrics with empirical risk minimization to speed up active learning on networked data," in KDD, 2009.
-
(2009)
KDD
-
-
MacSkassy, S.A.1
|