Approximate maximum entropy learning for classification: Comparison with other methods

Neural Networks for Signal Processing - Proceedings of the IEEE Workshop

Volumn , Issue , 2001, Pages 243-252

  Yan, Lian ^a   Miller, David J ^b  

^a Athene Software Inc   (United States)

^b The Pennsylvania State University   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

APPROXIMATION THEORY; ENTROPY; LEARNING SYSTEMS; VECTORS;

DEPENDENCE TREES;

MULTILAYER NEURAL NETWORKS;

EID: 0035784022     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (8)

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2. N. Friedman and M. Goldszmidt. Building classifiers using Bayesian networks. In Proc. of the National Conf. on AI, volume 2, pages 1277-1284, 1996.
3. E. Herskovits and G., Cooper. Kutato: an entropy-driven system for construction of probabilistic expert systems from databases. In Uncertainty in AI 6, pages 117 125, 1991.
4. H.H. Ku and S. Kullback. Approximating discrete probability distributions. IEEE Trans. on Info. Theory, 15(4):444-447, 1969.
5. J.C. Platt. Sequential minimal optimization: a fast algorithm for training support vector machines. Technical Report MSR-TR-98-14, Mierosoft Research, 1998.
6. D.J. Miller and L. Yan. Approximate maximum entropy joint feature inference consistent with arbitrary lower order probability constraints: application to statistical classification. Neural Computation, vol. 12, no. 9, pages. 2175-2208, 2000.
7. D.J. Miller and L. Yan. An Approximate Maximum Entropy Method for Classification and More General Inference: Relation to Other Maxent Methods and Nave Bayes. Proceedings of 34th Conference on information Sciences and Systems, vol. 1, pages 1-6, 2000.
8. L. Yen and D.J. Miller. General statistical inference for discrete and mixed spaces by an approximate application of the maximum entropy principle, IEEE Trans. on Neural Networks, vol. 11, no. 3, pp. 558-573, 2000.