Bayesian network classifiers with reduced precision parameters

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 7523 LNAI, Issue PART 1, 2012, Pages 74-89

  Tschiatschek, Sebastian ^a   Reinprecht, Peter ^a   Mücke, Manfred ^b,c   Pernkopf, Franz ^a  

^a GRAZ UNIVERSITY OF TECHNOLOGY   (Austria)

^b UNIVERSITY OF VIENNA   (Austria)

^c Sustainable Computing Research   (Austria)

Author keywords

Bayesian Network Classifiers; Custom precision Analysis; Discriminative Classifiers

Indexed keywords

BAYESIAN NETWORK CLASSIFIERS; BIT-WIDTH; CLASSIFICATION PERFORMANCE; CONDITIONAL PROBABILITIES; CUSTOM-PRECISION ANALYSIS; DIRECTED ACYCLIC GRAPH (DAG); DISCRIMINATIVE CLASSIFIERS; FLOATING POINT NUMBERS; OPTIMIZED PARAMETER; PROBABILISTIC CLASSIFIERS; REDUCED PRECISION;

BAYESIAN NETWORKS; DIRECTED GRAPHS; LEARNING SYSTEMS; OPTIMIZATION;

PROBABILITY;

EID: 84866885707     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-642-33460-3_10     Document Type: Conference Paper

References (17)

1. Acid, S., Campos, L.M., Castellano, J.G.: Learning Bayesian network classifiers: Searching in a space of partially directed acyclic graphs. Machine Learning 59, 213-235 (2005)
2. Bishop, C.M.: Pattern Recognition and Machine Learning (Information Science and Statistics). Springer (2007)
3. Chan, H., Darwiche, A.: When do numbers really matter? Artificial Intelligence Research 17(1), 265-287 (2002)
4. Chan, H., Darwiche, A.: Sensitivity analysis in Bayesian networks: From single to multiple parameters. In: Uncertainty in Artificial Intelligence (UAI), pp. 67-75 (2004)
5. Friedman, N., Geiger, D., Goldszmidt, M.: Bayesian network classifiers. Machine Learning, 131-163 (1997)
6. Guo, Y., Wilkinson, D., Schuurmans, D.: Maximum margin Bayesian networks. In: Uncertainty in Artificial Intelligence (UAI), pp. 233-242 (2005)
7. Haykin, S.: Neural Networks: A Comprehensive Foundation. Prentice-Hall, Upper Saddle River (1998)
8. Koller, D., Friedman, N.: Probabilistic Graphical Models: Principles and Techniques. MIT Press (2009)
9. LeCun, Y., Bottou, L., Bengio, Y., Haffner, P.: Gradient-based learning applied to document recognition. Proceedings of the IEEE 86(11), 2278-2324 (1998)
10. Muller, J.M., Brisebarre, N., de Dinechin, F., Jeannerod, C.P., Lefèvre, V., Melquiond, G., Revol, N., Stehlé, D., Torres, S.: Handbook of Floating-Point Arithmetic. Birkhäuser Boston (2010)
11. Overton, M.L.: Numerical computing with IEEE floating point arithmetic - including one theorem, one rule of thumb, and one hundred and one exercices. Society for Industrial and Applied Mathematics (SIAM) (2001)
12. Pearl, J.: Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference. Morgan Kaufmann Publishers Inc., San Francisco (1988)
13. Pernkopf, F., Wohlmayr, M., Tschiatschek, S.: Maximum margin Bayesian network classifiers. IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI) 34(3), 521-531 (2012)
14. Pernkopf, F., Bilmes, J.A.: Efficient heuristics for discriminative structure learning of Bayesian network classifiers. Journal of Machine Learning Research (JMLR) 11, 2323-2360 (2010)
15. Roos, T., Wettig, H., Grünwald, P., Myllymäki, P., Tirri, H.: On discriminative Bayesian network classifiers and logistic regression. Machine Learning 59(3), 267-296 (2005)
16. Vapnik, V.N.: Statistical Learning Theory. Wiley (1998)
17. Wang, H.: Using sensitivity analysis for selective parameter update in Bayesian network learning. In: Association for the Advancement of Artificial Intelligence, AAAI (2002)