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Tomás Kocka: [Publications] [Author Rank by year] [Co-authors] [Prefers] [Cites] [Cited by]
Publications of Author
- Gytis Karciauskas, Finn Verner Jensen, Tomás Kocka
Parameter Reusing in Learning Latent Class Models. [Citation Graph (0, 0)][DBLP]
AMAI, 2004, pp:- [Conf] - Tomás Kocka, Nevin Lianwen Zhang
Effective Dimensions of Partially Observed Polytrees. [Citation Graph (0, 0)][DBLP]
ECSQARU, 2003, pp:184-195 [Conf] - Nevin Lianwen Zhang, Tomás Kocka
Efficient Learning of Hierarchical Latent Class Models. [Citation Graph (0, 0)][DBLP]
ICTAI, 2004, pp:585-593 [Conf] - Petr Berka, Vladimír Las, Tomás Kocka
Rule Induction for Click-Stream Analysis: Set Covering and Compositional Approach. [Citation Graph (0, 0)][DBLP]
Intelligent Information Systems, 2005, pp:13-22 [Conf] - Tomás Kroupa, Petr Berka, Tomás Kocka
WISECON - An Intelligent Assistant for Buying Computers on the Internet. [Citation Graph (0, 0)][DBLP]
ISMIS, 2002, pp:167-175 [Conf] - Tomás Kocka, Remco R. Bouckaert, Milan Studený
On characterizing Inclusion of Bayesian Networks. [Citation Graph (0, 0)][DBLP]
UAI, 2001, pp:261-268 [Conf] - Tomás Kocka, Robert Castelo
Improved learning of Bayesian networks. [Citation Graph (0, 0)][DBLP]
UAI, 2001, pp:269-276 [Conf] - Tomás Kocka, Nevin Lianwen Zhang
Dimension Correction for Hierarchical Latent Class Models. [Citation Graph (0, 0)][DBLP]
UAI, 2002, pp:267-274 [Conf] - Jens D. Nielsen, Tomás Kocka, José Manuel Peña
On Local Optima in Learning Bayesian Networks. [Citation Graph (0, 0)][DBLP]
UAI, 2003, pp:435-442 [Conf] - Tao Chen, Tomás Kocka, Nevin Lianwen Zhang
Effective dimensions of partially observed polytrees. [Citation Graph (0, 0)][DBLP]
Int. J. Approx. Reasoning, 2005, v:38, n:3, pp:311-332 [Journal] - Nevin Lianwen Zhang, Tomás Kocka
Effective Dimensions of Hierarchical Latent Class Models. [Citation Graph (0, 0)][DBLP]
J. Artif. Intell. Res. (JAIR), 2004, v:21, n:, pp:1-17 [Journal] - Robert Castelo, Tomás Kocka
On Inclusion-Driven Learning of Bayesian Networks. [Citation Graph (0, 0)][DBLP]
Journal of Machine Learning Research, 2003, v:4, n:, pp:527-574 [Journal]
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