Edited Books and Journal Special Issues

1. D.J. Miller and D. Erdogmus, edited Journal of VLSI Signal Processing, special issue on 2005 IEEE Workshop on Machine Learning for Signal Processing, 2007.
2. Machine Learning for Signal Processing 2005, Proceedings of the 2005 IEEE Signal Processing Society Workshop, edited by V. Calhoun, T. Adali, J. Larsen, D. J. Miller, S. Douglas. (CDROM)
3. D. J. Miller, T. Adali, M. Van Hulle, J. Larsen, edited Journal of VLSI Signal Processing, vol. 37, nos. 2-3, June/July 2004.
4. Neural Networks for Signal Processing XI, Proceedings of the 2001 IEEE Signal Processing Society Workshop, edited by D. J. Miller, T. Adali, J. Larsen, M. Van Hulle, and S. Douglas, New York, 2001.

 

Journals

1. D. J. Miller and S. Pal, “Transductive methods for the distributed ensemble classification problem,” Neural Computation, vol. 19, no. 3, pp. 856-884, March 2007.
2. D. J. Miller and D. Erdogmus, “Guest Editorial for Special Issue on the 2005 IEEE Workshop in Machine Learning for Signal Processing,” Journal of VLSI Signal Processing Systems, 2007.
3. S. Pal and D. J. Miller, “An extension of iterative scaling for decision and data aggregation in ensemble classification,” Journal of VLSI Signal Processing Systems, 2007.
4. D. Bazell, D. J. Miller, and M. Subbarao, “Objective subclass determination of Sloan digital sky survey unknown spectral objects,” The Astrophysics Journal, vol. 649, no. 2, pp. 678-691, October 2006.
5. J. Wang, D. J. Miller, and G. Kesidis, “Efficient mining of the multidimensional traffic cluster hierarchy for digesting, visualization, and anomaly identification,” IEEE Journal on Selected Areas in Communications Special issue on High-speed Network Security; vol. 24, no. 10, pp. 1929-1941, October 2006.
6. M. W. Graham and D. J. Miller, “Unsupervised learning of parsimonious mixtures on large spaces with integrated feature and component selection,” IEEE Transactions on Signal Processing, vol. 54, no. 4, pp. 1289-1303, April 2006.
7. Q. Zhao and D. J. Miller, “Mixture modeling with pairwise instance-level class constraints,” Neural Computation, 17(11): 2482-2507, Nov. 2005.
8. D. Bazell and D. J. Miller, “Class discovery in galaxy classification,” The Astrophysical Journal, 618:723-732, 2005.
9. D. J. Miller, E. Carotti, Y. Wang , and J. De Martin, “Joint source-channel decoding of predictively and non-predictively encoded sources: a two-stage estimation approach,” IEEE Transactions on Communications, vol. 52, no. 9, pp. 1575-1584, 2004.
10. J. Browning and D. J. Miller, “A maximum entropy approach for collaborative filtering,” Journal of VLSI Signal Processing, vol. 37, pp. 199-209, 2004.
11. D. J. Miller, A. Tulay, J. Larsen, M. Van Hulle, “Guest Editorial for Special Issue on Machine Learning for Signal Processing,” Journal of VLSI Signal Processing Systems, vol. 37, No. 2-3, pp. 171-175, 2004.
12. R. Bajcsy, D.J. Miller, et al., “Cyber defense technology networking and evaluation,” Communications of the ACM, vol. 47, no. 3, pp. 58-61, 2004.
13. D. J. Miller and J. Browning, “A mixture model and EM-based algorithm for class discovery, Classification, and outlier rejection in mixed labeled/unlabeled data sets”, IEEE Transaction on Pattern Analysis and Machine Intelligence, vol. 25, no. 11, pp. 1468-1483, 2003.
14. D. J. Miller and Q. Zhao, “A sequence-based extension of mean-field annealing using the Forward/Backward algorithm: application to image segmentation,” IEEE Transaction on Signal Processing, vol. 51, No. 10, pp. 2692-2705, 2003.
15. P. Bunyayaratavej and D. J. Miller, “An iterative hill-climbing algorithm for discrete optimization on images: application to joint encoding of image transform coefficients, IEEE Signal Processing Letters, vol. 9, no. 2, 2002, pp. 46-50, 2003.
16. T. Kim, R. E. Van Dyck and David J. Miller, “Hybrid fractal zerotree wavelet image coding,” Signal Processing: Image Communication, vol. 17, pp. 347-360, 2002.
17. L. Yan and D. J. Miller, “General Statistical Inference for discrete and mixed spaces by an approximate application of the maximum entropy principle,” IEEE Transaction On Neural Networks, special issue on Data Mining and Knowledge Discovery, vol. 11, no. 3, pp. 558-573, May 2000.
18. M. Park and D. J. Miller, “Joint source-channel decoding for variable-length encoded data by exact and approximate MAP sequence estimation,” IEEE Transaction on Communications, pp. 1-6, January 2000.
19. 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, pp. 2175-2208, 2000.
20. R. E. Van Dyck and D. J. Miller, “Transport of wireless video using separate, concatenated, and joint source-channel coding,” Proceedings of the IEEE, pp 134-1750, Oct. 1999.
21. D. J. Miller and L. Yan, “Critic-driven ensemble classification,” IEEE Trans. On Signal Processing, 1999, pp. 2833-2844, October 1999.
22. M. Park and D. J. Miller, “Improved image decoding using minimum mean-squared estimation and a Markov mesh,” IEEE Trans. on Image Processing, pp. 863-867, June 1999.
23. A. Rao, D. J. 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, vol. 21, no. 2, pp. 159-173, February 1999.
24. D. J. Miller and H. S. Uyar, “Combined Learning and Use for a Mixture Model Equivalent to the RBF Classifier,” Neural Computation, vol. 10, no. 2, pp. 281-294, 1998.
25. D. J. Miller and M. Park, “A Sequence-Based Approximate MMSE Decoder for Source Coding Over Noisy Channels Using Discrete Hidden Markov Models,” IEEE Transaction on Communications, pp. 222-231, February 1998.
26. D. J. Miller and H. S. Uyar, “A Mixture of Experts Classifier with Learning Based on Both Labeled and Unlabeled Data,” Neural Information Processing Systems, 9:571-577, 1997.
27. A. V. Rao, D. J. Miller, K. Rose, and A. Gersho, “Mixture of Experts Regression Modeling by Deterministic Annealing,” IEEE Transaction on Signal Processing, vol. 45, no. 11, pp. 2811-2820, November 1997.
28. M. Park and D. J. Miller, “Low-delay Optimal MAP State Estimation in HMM’s with Application to Symbol Decoding,” IEEE Signal Processing Letters, vol. 4, no. 10, pp. 289-292, October 1997.
29. D. J. Miller, A. Rao, K. Rose, and A. Gersho, “A Global Optimization Technique for Statistical Classifier Design,” IEEE Transaction on Signal Processing, December 1996.
30. K. Rose, D. J. Miller, and A. Gersho, “Entropy-constrained Tree-structured Vector Quantizer Design,” IEEE Transaction on Image Processing, 5(2):393-397, February 1996.
31. D. J. Miller and K. Rose, “Hierarchical, Unsupervised Learning with Growing via Phase Transitions,” Neural Computation, 8:425-450, February 1996.
32. D. J. Miller, A. Rao, K. Rose, and A. Gersho, “An Information-theoretic Learning Algorithm for Neural Network Classification,” Neural Information Processing Systems, 8:591-597, 1996.
33. D. Miller and K. Rose, “A non-greedy approach to tree-structured clustering,” Pattern Recognition Letters, vol. 15, pp. 683-690, July 1994.
34. D. Miller and K. Rose, “Combined source-channel vector quantization using deterministic annealing,” IEEE Transaction on Communications, vol. 42, pp. 347-356, Feb. 1994.

 

 

Conferences & Workshops

1. A. Nag, D. J. Miller, A. Brown, and K. Sullivan, “A system for vehicle recognition in video based on SIFT features, mixture models, and support vector machines,” SPIE Defense and Security Symposium, Orlando, FL, April 2007.
2. J. Wang, G. Kesidis, and D. J. Miller, “New Directions in Covert Malware Modeling Which Exploit White-Listing”, In Proceedings of the IEEE Sarnoff Symposium on Communication, Princeton, NJ, May 2007.
3. Y. Feng, Z. Wang, Y. Zhu, J. Xuan, D. J. Miller, R. Clarke, E. Hoffman, and Y. Wang, “Learning the tree of phenotype using genomic data and VISDA,” IEEE Symposium on Bioinformatics and Bioengineering, 2006.
4. J. Wang, I. Hamedeh, G. Kesidis, and D. J. Miller, “Polymorphic worm detection and defense: system design, experimental methodology, and data resources,” SIGCOMM Workshop on Large Scale Attack Defense (LSAD), 2006.
5. J. Wang, D. J. Miller, and G. Kesidis, “Multidimensional flow mining for digesting, visualization, and signature extraction,” DETER Community Workshop, Arlington, VA, June 15-16, 2006.
6. A. Brown, K. Sullivan, and D. J. Miller, “Feature-aided multiple target tracking in the image plane, Proceedings of the SPIE, Intelligent Computing: Theory and Applications IV, vol. 6229, pp. 62290Q, 2006.
7. D. J. Miller, S. Pal, and Y. Wang, “Constraint-based transductive learning for distributed ensemble classification,” IEEE Workshop on Machine Learning for Signal Processing, pp. 15-20, Maynooth, Ireland, September 2006.
8. D. J. Miller and S. Pal, “Transductive methods for distributed ensemble classification,” Conference on Information Sciences and Systems, pp. 1605-1610, Princeton, NJ, March 2006.
9. Q. Zhao and D. J. Miller, “Semisupervised learning of mixture models with class constraints,” IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP), MLSP-L2.1, Finalist for Best Student Paper Award, 2005.
10. D. J. Miller, S. Pal, and Q. Zhao, “A latent variable extension of iterative scaling for classification on continuous and mixed continuous-discrete feature spaces,” Conference on Information Sciences and Systems (CISS), TA2-7, Johns Hopkins University, Baltimore, MD, 2005.
11. D. J. Miller and S. Pal, “An extension of iterative scaling for joint decision-level and feature-level fusion in ensemble classification,” IEEE Workshop on Machine Learning for Signal Processing, pp. 61-66, Mystic, CT, 2005.
12. S. Jiwasurat, G. Kesidis, D. J. Miller, “Hierarchical shaped deficit round-robin scheduling,” Proceedings Global Telecommunications Conference, Vol. 2:6, 2005.
13. Q. Zhao and D. J. Miller, “A deterministic, annealing-based approach for learning and model selection in finite mixture models,” Proc. of IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP), Montreal, Canada, 2004.
14. M. Graham and D J. Miller, “Unsupervised learning of mixtures on huge feature spaces with integrated feature and component selection,” Proceedings of Smart Engineering and System Design (ANNIE), St. Louis, MO, 2004.
15. Y. Wang and D. J. Miller, “High order JSC decoding and conditional entropy encoding of images with reduced complexity via improved iterative scaling,” Proceedings of Conference on Information Sciences and Systems (CISS), Princeton, NJ, 2004.
16. Y. Wang , E. Carotti, D. J. Miller, and J.C. De Martin, “A two-stage estimation approach for bridging the performance complexity gap in joint source-channel decoding,” Proceedings of Conference on Information Sciences and Systems, Princeton, NJ, 2004.
17. D. J. Miller and J. Browning, “A mixture model framework for class discovery and outlier detection in Mixed labeled/unlabeled data sets,” Proceedings of the IEEE Workshop on Neural Networks for Signal Processing, Toulouse, France, 2003.
18. D. J. Miller and J. Browning, “A mixture model and EM algorithm for robust classification, outlier rejection, and class discovery,” Proceedings of IEEE International Conference on Acoustics, Speech, and Signal Processing, Hong Kong, China, 2003.
19. D. J. Miller, P. Bunyaratavej and Qi Zhao, “A sequence-based generalization of mean-field annealing using the Forward/Backward algorithm: application to image segmentation,” Proceedings of IEEE International Conference on Acoustics, Speech, and Signal Processing, vol., 1, pp. 969-972, Orlando, FL, 2002.
20. D. J. Miller, Qi Zhao and Piya Bunyaratavej, “A sequence-based extension of mean field annealing using the Forward/Backward algorithm,” Proceedings of Conference on Information Sciences and Systems, Princeton, NJ, 2002.
21. J. Browning and D. J. Miller, “A maximum entropy approach for collaborative filtering,” IEEE Workshop on Neural Networks for Signal Processing, 2001, pp.3-12, Falmouth, MA, 2001.
22. L. Yan and D. J. Miller, “Approximate maximum entropy learning for classification: comparison with other methods,” IEEE Workshop on Neural Networks for Signal Processing, pp.243-252, Falmouth, MA, 2001.
23. L. Yan and D. J. Miller, “Critic-driven ensemble classification via a learning method akin to boosting,” Proceedings of Smart Engineering and System Design (ANNIE), 2001.
24. P. Bunyaratavej and D. J. Miller, “Locally optimal joint encoding of image transform coefficients,” Proceedings of IEEE International Conference on Acoustics, Speech, and Signal Processing, pp. 2573-2576, Salt Lake City, UT, 2001.
25. A. Ravindran, H. Liu, I. Agoren, A. Lackpour, D. J. Miller, M. Kavehrad, J. Doherty, “Mobile multimedia services for third generation communications systems,” Proc. of the IEEE Vehicular Technology Conference, pp. 2589-2593, Atlantic City, NJ, 2001.
26. H. Liu, A. Ravindran, D. J. Miller, M. Kavehrad, J. F. Doherty, I. Agoren, A. Lackpour, “Error-resilient H.263 video coding for wideband CDMA systems,” Proceedings of the 35 th Asilomar Conference on Signals, Systems, and Computers, Pacific Grove, CA, 2001
27. D. J. Miller and L. Yan, “An approximate maximum entropy method for feature inference: relation to other maximum entropy methods and to naive Bayes,” Princeton Conference on Information Sciences and Systems, Princeton, NJ, pp. 1-6, April 2000.
28. D. J. Miller and D. L. Hall, “The use of automated critics to improve the fusion of marginal sensors for Automatic Target Recognition and Identification Friend-Foe- Neutral applications,” National Symposium on Sensor and Data Fusion, Baltimore, MD, May 1999.
29. D. J. Miller and L. Yan, “An approximate maximum entropy method for feature inference: relation to other maximum entropy methods and to na?ve Bayes,” Princeton Conference on Information Sciences and Systems, Princeton, NJ, pp. 1-6, April 2000.
30. D. J. Miller and L. Yan, “Some analytical results on critic-driven ensemble classification,” Proceedings of the IEEE Workshop on Neural Networks for Signal Processing, Madison, WI, pp. 253-262, Sept. 1999.
31. L. Yan and D. J. Miller, “General statistical inference by an approximate application of the maximum entropy principle,” Proceedings of the IEEE Workshop on Neural Networks for Signal Processing, pp. 112-121, Sept. 1999.
32. D. J. Miller and L. Yan, “Approximate maximum entropy joint feature inference for discrete space classification,” Proceedings of International Joint Conference on Neural Networks, June 1999, Washington, D.C. Paper no 253 in the CD-ROM Proceedings.
33. M. Park and D. J. Miller, “Joint source-channel decoding for variable-length encoded data by exact and approximate MAP sequence estimation,” Proceedings of IEEE International Conference On Acoustics, Speech, and Signal Processing, Phoenix, AZ, pp. 1-6, April 1999.
34. D. J. Miller and L. Yan, “Ensemble classification by critic-driven combining,” Proceedings of IEEE International Conference on Acoustics, Speech, and Signal Processing, Phoenix, AZ, pp. 1029-1032, April 1999.
35. L. Yan and D. J. Miller, “Time series prediction by neural network inversion,” Proceedings of IEEE International Conference on Acoustics, Speech, and Signal Processing, Phoenix, AZ, pp. 1049-1052, April 1999.
36. M. Park and D. J. Miller, “Improved joint source-channel decoding for variable-length encoded data using soft decisions and MMSE estimation,” Proceedings of Data Compression Conference, Snowbird, Utah, p. 554, March 1999.
37. M. Park and D. J. Miller, “Decoding Entropy-Coded Symbols Over Noisy Channels by MAP Sequence Estimation for Asynchronous HMMs,” Proceedings of Conference on Information Science and Systems, Princeton, NJ, pp. 477-482, 1998.
38. J. Roh and D. J. Miller, “A new set partitioning algorithm for wavelet-based image coding,” Proceedings of IEEE International Conference on Image Processing, Chicago, IL, pp. 102-106, 1998.
39. A. Rao, D. Miller, K. Rose, and A. Gersho, “Deterministically Annealed Mixture of Experts Models for Statistical Regression,” Proceedings of IEEE International Conference on Acoustics, Speech, and Signal Processing, Munich, Germany, pp. 3201-3204.
40. D. J. Miller and M. Park, “Sequence-Based MMSE Source Decoding Over Noisy Channels Using Discrete HMMs,” Proceedings of Conference on Information Science and Systems, Baltimore, MD, pp. 325-330, 1997.
41. M. Park and D. J. Miller, “Low-delay, Optimal MAP and Minimum-cost State Estimation in HMMs with Application to Symbol Decoding,” Proceedings of Conference on Information Science and Systems, Baltimore, MD, pp. 556-561, 1997.
42. D. J. Miller, H. Uyar, and L. Yan, “Combined Learning and Use for Classification and Regression Models,” Proceedings of IEEE Workshop on Neural Networks for Signal Processing, Amelia Island, pp. 102-111.
43. M. Park and D. J. Miller, “Image Decoding Over Noisy Channels Using Minimum Mean-Squared Estimation and a Markov Mesh,” Proceedings of IEEE International Conference on Image Processing, Santa Barbara, CA, pp. 594-597, 1997.
44. D. J. Miller and S. Uyar, “A generalized Gaussian mixture classifier with learning based on both labelled and unlabelled data,” Proceedings of Conference on Information Science and Systems, Princeton, NJ, pp. 783-787, 1996.
45. A. Rao, D. J. Miller, K. Rose, and A. Gersho, “A Generalized VQ Method for Combined Compression and Estimation,” Proceedings of the IEEE International Conference on Acoustics, Speech and Signal Processing, 4, pp. 2032-2035, April 1996.
46. D. Miller, A. Rao, K. Rose, and A. Gersho, “An information-theoretic learning algorithm for neural network classification,” Neural Information Processing Systems, pp. 591-59, Dec. 1995.
47. D. J. Miller, A. Rao, K. Rose, and A. Gersho, “An information-theoretic framework for optimization with application to supervised learning,” IEEE International Symposium on Information Theory, p. 257, Sept. 1995.
48. A. Rao, D. J. Miller, K. Rose, and A. Gersho, “Generalized Vector Quantization -- jointly optimal estimation and quantization,” IEEE International Symposium on Information Theory, p. 432, Sept. 1995.
49. D. Miller, A. Rao, K. Rose, and A. Gersho, “A maximum entropy approach for optimal statistical classification,” Proceedings of the IEEE Workshop on Neural Networks for Signal Processing, Boston, MA, pp. 58-66, Sept. 1995.
50. A. Rao, D. J. Miller, K. Rose, and A. Gersho, “An information-theoretic approach for statistical regression with model growth by bifurcations,” Proceedings of the 27th Symposium on the Interface, pp. 220-22, June 1995.