Dr. David J. Miller's List of Publications

JOURNAL PUBLICATIONS

• 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, 2000, pp. 2175-2208.

• L. Yan and D. J. Miller, ``General statistical inference for discrete and mixed feature spaces by an approximate application of the maximum entropy principle'', IEEE Transactions on Neural Networks: special issue on Data Mining for Knowledge Discovery, Vol. 11, No. 3, 2000, pp. 558-573.

• M. Park and D. Miller, ``Joint source-channel decoding for variable-length encoded data by exact and approximate {MAP} sequence estimation'', IEEE Trans. on Communications, Vol. 48, No. 1, 2000, pp. 1-6.

• D. J. Miller and L. Yan, ``Critic-driven ensemble classification'', IEEE Trans. on Signal Processing, Oct. 1999, pp. 2833-2844.

• R. E. Van Dyck and D. Miller, ``Transport of wireless video using separate, concatenated, and joint source-channel coding'', Proceedings of the IEEE, Oct. 1999, pp. 1734-1750.

• A. Rao, D. Miller, K. Rose, and A. Gersho, ``A deterministic annealing approach for parsimonious design of piecewise regression models'', IEEE Trans. on Pattern Analysis and Machine Intelligence, Feb. 1999, pp. 159-173.

• M. Park and D. Miller, ``Improved image decoding using minimum mean-squared estimation and a Markov mesh'', IEEE Trans. on Image Processing, June 1999, pp. 863-867.

• D. Miller and H. Uyar, ``Combined learning and use for a mixture model equivalent to the RBF classifier'', Neural Computation, Vol. 10, No. 2, 1998, pp. 281-293.

• A. Rao, D. Miller, K. Rose, and A. Gersho, ``Mixture of Experts Regression Modeling by Deterministic Annealing'', IEEE Transactions on Signal Processing, Vol. 45, No. 11, Nov. 1997, pp. 2811-2820.

• D. Miller and M. Park, ``A sequence-based approximate MMSE decoder for source coding over noisy channels using discrete hidden Markov models'',  IEEE Trans. on Communications, Vol. 46, No. 2, Feb. 1998, pp. 222-231.

• M. Park and D. Miller, ``Low-delay optimal MAP state estimation in HMM's with application to symbol decoding'', IEEE Signal Processing Letters, Vol. 4, No. 10, Oct. 1997, pp. 289-292.

• D. Miller and K. Rose, “Hierarchical, unsupervised learning with growing via phase transitions'', Neural Computation, Vol. 8, 1996, pp. 425-450.

• D. Miller, A. Rao, K. Rose, and A. Gersho, ``A global optimization technique for statistical classifier design'', IEEE Transactions on Signal Processing, Vol. 44, No. 12, Dec. 1996, pp. 3108-3121.

• K. Rose, D. Miller, and A. Gersho, ``Entropy-constrained tree-structured vector quantizer design'', IEEE Trans. on Image Processing, Vol. 5, No. 2, Feb. 1996, pp. 393-397.

• D. Miller and K. Rose, ``A non-greedy approach to tree-structured clustering'', Pattern Recognition Letters, Vol. 15, July 1994, pp. 683-690.

• D. Miller and K. Rose, ``Combined source-channel vector quantization using deterministic annealing'', IEEE Trans. on Communications, Vol. 42, Feb. 1994, pp. 347-356.

SELECT CONFERENCE PUBLICATIONS

1. "Combined learning and use for classification and regression models", D. J. Miller, H.S. Uyar, and L. Yan. To appear in proceedings of IEEE Neural Networks for Signal Processing, 1997.

 
2. "A mixture of experts classifier with learning based on both labelled and unlabelled data", D. Miller and H.S. Uyar, to appear in Neural Information Processing Systems, 1997.

 
3. "Sequence-based MMSE source decoding over noisy channels using discrete HMMs", D. J. Miller and M. Park. To appear in proceedings of the Conference on Information Science and Systems, 1997.

 
4. "Low-delay, optimal MAP and minimum-cost state estimation in HMMs with application to symbol decoding", M. Park and D.J. Miller. To appear in proceedings of the Conference on Information Science and Systems, 1997.

 
5. "Image decoding over noisy channels using minimum mean-squared estimation and a Markov mesh", M. Park and D.J. Miller. To appear in proceedings of the IEEE International Conference on Image Processing, 1997.

 
6. "An information-theoretic learning algorithm for neural network classification", D. Miller, A. Rao, K. Rose, and A. Gersho, Neural Information Processing Systems, 1996.

 
7. "A maximum entropy approach for optimal statistical classification", D. Miller, A. Rao, K. Rose, and A. Gersho. Proceedings of IEEE Neural Networks for Signal Processing, 1995.

 
8. "Deterministic annealing for trellis quantizer and HMM design using Baum-Welch re-estimation", D. Miller, K. Rose, and P. A. Chou, IEEE ICASSP, 1994.