Robi Polikar Research

Edited Volumes / Book & Encyclopedia Chapters

1. Oza N., Polikar R., Kittler J., and Roli F., Editors, Multiple Classifier Systems (MCS 2005), Springer Lecture Notes in Computer Science (LNCS), vol. 3541, Berlin, Germany: Springer, 2005. ISBN: 3-540-26306-3

2. Polikar R., Pattern Recognition, In Wiley Encyclopedia of Biomedical Engineering, Ed. Akay, M., New York, NY: Wiley., 2006.
«««This is a tutorial paper «««

3. Polikar R., Keinert F., Greer M.H., “Wavelet analysis of event related potentials for early diagnosis of Alzheimer’s disease,” in Wavelets in Signal and Image Analysis, From Theory to Practice (ed. A. Petrosian and F.G. Meyer), pp. 453-478, Boston, MA: Kluwer Academic Publishers, 2001.

4. Polikar R., The story of wavelets, in Physics and Modern Topics in Mechanical and Electrical Engineering, (ed. Mastorakis, N), pp. 192-197, World Scientific and Eng. Society Press, 1999.

Journal Publications

1. Muhlbaier M., Topalis A., Polikar R., “Learn++.NC: Combining Ensemble of Classifiers Combined with Dynamically Weighted Consult-and-Vote for Efficient Incremental Learning of New Classes,” IEEE Transactions on Neural Networks, vol. 20, no. 1, pp. 152 – 168, 2009.

2. Merzagora A.C., Butti M., Polikar R., Izzetoglu M., Bunce S., Cerutti S., Bianchi A.M., Onaral B., “Model comparison for automatic characterization and classification of average ERPs using visual oddball paradigm,” Clinical Neurophysiology, vol 120., no. 2, pp. 264-274, 2009.

3. Polikar, R., “Ensemble learning,” Scholarpedia, vol. 4, no. 1, pp. 2776, 2009 (also see below for web publications).

4. Rosen G., Garbarine E., Caseiro D., Polikar R. and Sokhansanj B., “Metagenome fragment classification using N-mer frequency profiles,” Advances in Bioinformatics, vol. 2008, pp.1-12, 2008.

5. Cevikalp H. and Polikar R., “Local classifier weighting by quadratic programming,” IEEE Transactions on Neural Networks, vol. 19, no. 10, pp. 1832 – 1838, 2008.

6. Polikar R., Topalis A., Green D., Kounios J., Clark C.M., Ensemble based data fusion for early diagnosis of Alzheimer’s disease, Information Fusion, vol. 9, no. 1, pp. 83-95, 2008.

7. Polikar R., “Bootstrap inspired techniques in computational intelligence: ensemble of classifiers, incremental learning, data fusion and missing features, IEEE Signal Processing Magazine, vol. 24, pp. 59-72, 2007. «««This is a tutorial paper «««

8. Parikh D. and Polikar R., “An Ensemble based incremental learning approach to data fusion, IEEE Transactions on Systems, Man and Cybernetics, Part B, Cybernetics vol. 37, no. 2, pp. 437-450, 2007

9. Polikar R., Topalis A., Green D., Kounios J., Clark C.M., Comparative multiresolution analysis and ensemble of classifiers approach for early diagnosis of Alzheimer’s disease, Computers in Biology and Medicine, vol. 37, no. 4, 542-558, 2007 .

10. Polikar R., “Ensemble based systems in decision making,” IEEE Circuits and Systems Mag., vol. 6, no. 3, pp. 21-45 , 2006.
«««This is a tutorial paper «««

11. Polikar R., Jahan K. and Healy B., “A combined pattern separability and two-tiered classification approach for identification of binary mixtures of VOCs,” Sensors & Actuators (B), vol. 116, no:1-2, pp. 174-182, 2006.

12. Schmalzel J., Figueroa F., Morris J., Mandayam S., and Polikar R., “An Architecture for Intelligent Systems Based on Smart Sensors,” IEEE Transactions on Instrumentation and Measurement, vol. 54, no. 4, pp. 1612-1616, 2005.

13. Polikar R., Udpa L., Udpa S., Honavar V., “ An incremental learning algorithm with confidence estimation for automated identification of NDE signals,” IEEE Transactions on Ultrasonics, Ferroelectrics and Frequency Control, vol. 51, no. 8, pp. 990-1001, 2004.

14. Jahan K., Chen J., Mandayam S., Krchnavek R., Sukumaran B., Mehta Y., Kadlowec J., Von Lockette P., Polikar R., “Digital imaging experiences for undergraduate engineering students,” World Transactions on Engineering and Technology Education, vol. 3, no.2, pp. 227-230, 2004.

15. Das M., Shekhar H., Liu X., Polikar R., Ramuhalli P., Udpa L., Udpa S., “A generalized likelihood ratio technique for automated analysis of bobbin coil eddy current data,” NDT&E International vol. 35, no. 5, pp. 329-336, 2002.

16. Polikar R., Udpa L., Udpa, S., Honavar, V., “Learn++: An incremental learning algorithm for supervised neural networks,” IEEE Transactions on System, Man and Cybernetics (C), Special Issue on Knowledge Management, vol. 31, no. 4, pp. 497-508, 2001

17. Polikar R., Shinar R., Udpa L., Porter M.D., “Artificial intelligence methods for selection of an optimized sensor array for identification of volatile organic compounds,” Sensors and Actuators (B), vol. 80, no. 3, pp. 243-254, 2001.

18. Shekhar, H.; Polikar, R.; Ramuhalli, P.; Liu, X.; Das, M.; Udpa, L.; Udpa, S.S., “Dynamic thresholding for automated analysis of bobbin probe eddy current data,” Int. J. of Applied Electromagnetics and Mechanics, vol. 15, no. 1-4, pp. 39-46, SPEC, 2001-2002.

19. Simone, G., Morabito, F.C.; Polikar, R.; Ramuhalli, P.; Udpa, L.; Udpa, S., “Feature extraction techniques for ultrasonic signal classification,” Int. J. of Applied Electromagnetics and Mechanics, vol. 15, no. 1-4, pp. 291-294, SPEC 2001-2002.

20. Xiang P., Ramakrishnan S., Cai X., Ramuhalli P., Polikar R., Udpa S.S., Udpa L., “Automated analysis of rotating probe multi-frequency eddy current data from steam generator tubes,” International Journal of Applied Electromagnetics and Mechanics, vol. 12, no. 3-4, pp. 151-164, 2000.

21. Spanner J., Udpa L., Polikar R., Ramuhalli P., Neural networks for ultrasonic detection of inter-granular stress corrosion cracking, The e-Journal of Nondestructive Testing & Ultrasonics, vol. 5, no. 7, 2000.

22. Polikar R., Udpa L., Udpa S.S., Taylor T., Frequency invariant classification of ultrasonic weld inspection signals, IEEE Transactions on Ultrasonics, Ferroelectrics, and Frequency Control, vol. 45, no. 3, pp. 614-625, 1998.

Peer-reviewed Web Publications

1. Polikar R., “Ensemble Learning,” Scholarpedia, Peer Reviewed Free Encyclopedia, Available at: http://www.scholarpedia.org/article/Ensemble_learning

Other Book Chapters &

Proceedings Published In Edited Book Volumes

1. Elwell R. and Polikar R.,” Incremental Learning of Variable Rate Concept Drift,” Multiple Classifier Systems (MCS 2009), Lecture Notes in Computer Science, J.A. Benediktsson et al, eds., vol. 5519, pp. 142-151, Reykjavik, Iceland, June 2009—to appear.

2. DePasquale, J. and Polikar R., “Random feature subset selection for ensemble based classification of data with missing features,” 7th Int. Workshop on Multiple Classifier Systems, in Lecture Notes in Computer Science, vol. 4472, pp. 251-260, Springer, 2007.

3. Muhlbaier, M.D., and Polikar, R., “An ensemble approach for incremental learning in nonstationary environments,” 7th Int. Workshop on Multiple Classifier Systems, in Lecture Notes in Computer Science, vol. 4472, pp. 490-500, Berlin: Springer, 2007.

4. Syed-Mohammed H., Leander J., Marbach M., and Polikar R., “Can AdaBoost.M1 learn incrementally? A comparison to Learn++ under different combination rules,” Int. Conf. on Artificial Neural Networks (ICANN2006), Lecture Notes in Computer Science (LNCS) , vol. 4131, pp. 254-263, Athens, Greece. Berlin: Springer, 2006.

5. Erdem Z., Polikar R., Gurgen F. , Yumusak N., “Reducing the Effect of Out-Voting Problem in Ensemble Based Incremental Support Vector Machines” 15th Int. Conf. on Artificial Neural Networks (ICANN 2005): Lecture Notes in Computer Science (LNCS), vol. 3697, pp. 607-612, Warsaw, Poland. Berlin: Springer, 2005.

6. Erdem Z., Polikar R., Yumusak N., Gurgen F., “ Classification of VOCs with incremental SVMs and RBF Networks,” ISCIS 2005, Springer Lecture Notes in Computer Science (LNCS), vol. 3733, pp. 322-331, Istanbul, Turkey, October 2005.

7. Muhlbaier M., Topalis A., Polikar R., “Ensemble confidence estimates posterior probability,” 6th Int. Workshop on Multiple Classifier Systems (MCS 2005), Springer Lecture Notes in Computer Science (LNCS), vol. 3541, pp. 326-335, Seaside, CA, June 2005.

8. Erdem Z., Polikar R., Gurgen F., Yumusak N., “Ensemble of SVM Classifiers for Incremental Learning,” 6th Int. Workshop on Multiple Classifier Systems (MCS 2005), Springer Lecture Notes in Computer Science (LNCS), vol. 3541, pp. 246-255, Seaside, CA, June 2005.

9. Muhlbaier M., Topalis A., Polikar R., “Learn++.MT: A new approach to incremental learning,” 5th Int. Workshop on Multiple Classifier Systems (MCS 2004), Springer LINS vol. 3077 , pp. 52-61, Cagliari, Italy, June 2004.

10. Lewitt M. and Polikar R., “An ensemble approach for data fusion with Learn++,” 4th Int. Workshop on Multiple Classifier Systems (MCS 2003), Springer LINS vol. 2709 , pp. 176-185Surrey, England, June 11-13 2003.

11. Byorick J. and Polikar R., “Confidence estimation using incremental learning algorithm, Learn++,” Int. Conf. on Artificial Neural Networks (ICANN 2003), Springer LINS vol. 2714, pp. 181 – 188, Istanbul, Turkey, 26-29 June 2003.

12. Eckerd R., Neyhart J., Polikar R., Mandayam S., Tseng M., “Neural and decision theoretic approaches for the automated segmentation of radiodense tissue in digitized mammograms,” Review of Progress in Quantitative Nondestructive Evaluation (QNDE 2002), vol. 22B, pp. 1735-1742, Bellingham, WA, July 2002, published by American Institute of Physics, 2003.

13. Polikar R., “Incremental learning of NDE signals with confidence estimation,” Review of Progress in Quantitative Nondestructive Evaluation (QNDE 2001), vol. 21A, pp. 744-751, Brunswick, ME, 29 July – 3 August 2001, published by American Institute of Physics, 2002.

14. Neyhart J.T., Kirlakovsky M., Coleman K.M., Polikar R., Tseng M., Mandayam S.A., “Automated segmentation and quantitative characterization of radiodense tissue in digitized mammograms,” Review of Progress in Quantitative Nondestructive Evaluation (QNDE 2001), vol. 21B, pp. 1866-1879, Brunswick, ME, July 2001, published by American Institute of Physics, 2002.

15. Polikar R., Udpa L., Udpa S., Incremental learning of ultrasonic weld inspection signals, Proc. of 27th Review of Progress in Quantitative Nondestructive Evaluation (QNDE 2000), Ames, IA, vol.20A, pp. 857- 863, 2000.

16. Polikar R., Udpa L., Udpa, S.S., Time scaling and frequency invariant classification of ultrasonic NDE signals, Proc. of 24th Review of Progress in Quantitative Nondestructive Evaluation, Vol. 17A, pp. 743-749, San Diego CA, 1998.

Peer Reviewed Conference Proceedings

1. Elwell R. and Polikar R., “Incremental Learning in Nonstationary Environments with Controlled Forgetting,” Int. Joint Conference on Neural Networks (IJCNN 2009), Atlanta, GA June 2009, to appear.

2. Ahiskali M., Polikar R., Kounios J., Green D., Clark C.M., “Combining Multichannel ERP Data for Early Diagnosis of Alzheimer’s Disease,” IEEE Neuroengineering Conference 2009, pp. 522-525, Antalya, Turkey, May 2009.

3. Ahiskali M., Green D., Kounios J., Clark C.M, Polikar R., ERP based decision fusion for AD diagnosis across cohorts, IEEE Engineering in Medicine and Biology Conference (EMBC 2009), Minneapolis, MN, September 2009—to appear.

4. Karnick M., Muhlbaier M., Polikar R., “Incremental Learning in Non-stationary Environments with Concept Drift using a Multiple Classifier Based Approach,” International Conference on Pattern Recognition (ICPR2008), Tampa, FL, December 2008 – to appear.

5. Patel T., Polikar R., Davatzidos C. and Clark C., “EEG and MRI data fusion for early diagnosis of Alzheimer’s disease,” IEEE Engineering in Medicine and Biology Conference (EMBC 2008) , pp. 1757-1760, Vancouver, BC, Canada, August 2008.

6. Cevikalp H., Triggs B., and Polikar R., “ Nearest hyperdisk methods for high dimensional classification,” International Conference on Machine Learning (ICML 2008), Helsinki, Finland, July 2008.

7. Cevikalp H., Triggs B., Frederic J. and Polikar R., “Margin-Based Discriminant Dimensionality Reduction for Visual Recognition,” Computer Vision and Pattern Recognition (CVPR 2008), pp. 1-8, Anchorage, AK 23-28 June 2008.

8. Karnick M., Ahiskali M., Muhlbaier M., Polikar R., “Learning Concept Drift in Nonstationary Environments Using an Ensemble of Classifiers Based Approach,” IEEE World Congress on Computational Intelligence, pp. 3455-3462, Hong Kong, June 2008.

9. Merzagora A., Shewokis P., Bunce S., Shultheis M., Izzetoglu K., Izzetoglu M., Polikar R., Onaral B., “Combined fNIRs and EEG for the assessment of cognitive impairments following traumatic brain injury,” (abstract only) Society of Applied Neuroscience Meeting (SAN 2008), Seville, Spain May 2008.

10. Balut B., Karnick M., Green D., Kounios J., Clark C., and Polikar R., “Ensemble based data fusion from parietal region of event related potentials for early diagnosis of Alzheimer’s disease,” Proc. Int. Joint Conf. on Neural Networks, pp. 2409-2414, Orlando, Fl, July 2007.

11. Depasquale J., and Polikar R., “Random feature subset selection for analysis of data with missing features,” Proc. Int. Joint Conf. on Neural Networks, pp. 2379-2384, Orlando, Fl, July 2007.

12. Muhlbaier M., Polikar R., “Multiple classifiers based incremental learning algorithm for learning nonstationary environments,” IEEE International Conference on Machine Learning and Cybernetics (ICMLC 2007), pp. 3618-3623, Hong Kong, China, August 2007.

13. Polikar R., Ramachandran R., Head L., Tahamont M., “Introducing multidisciplinary novel content through laboratory exercises on real world applications,” ASEE Annual Conference and Exposition, Honolulu, HI, 2007.

14. Gandhi H., Green D., Kounios J., Clark C.M., Polikar R., “Stacked generalization for early diagnosis of Alzheimer’s Disease,” 28th Int. Conf. of the IEEE Engineering in Medicine and Biology Society (EMBC 2006), New York, NY September 2006.

15. R. Polikar, H. Syed-Mohammed, J. Leander, M. Marbach, “Ensemble Techniques for Incremental Learning of New Concept Classes under Hostile Non-stationary Environments,” IEEE Int. Conf. on Systems, Man and Cybernetics, Taipei, Taiwan, October 2006

16. Polikar R., Ramachandran R., Head L.M. and Tahamont M., “Laboratory Integration of Emerging Topics into Existing Curriculum,” IEEE Frontiers in Education Conf. , Session M1D, pp. 21-26, San Diego, CA – October 2006

17. N. Stepenosky, J. Kounios, C. Clark and R. Polikar, “Ensemble Techniques with Weighted Combination Rules for Early Diagnosis of Alzheimer's Disease,” IEEE Int. Joint Conf. on Neural Networks (IJCNN2006), pp. 1935-1942, Vancouver, Canada—July 2006

18. H.Syed-Mohammed and R. Polikar, “A random subspace method for missing data,” 6th Int. Conf. on Recent Advances in soft computing, Canterbury, England, 2006 (abstract published only).

19. H.Syed-Mohammed and R. Polikar, “The Impact of Distribution Update Rule in Boosting Based Approaches for Incremental Learning of New Concept Classes,” 6th Int. Conf. on Recent Advances in soft computing, Canterbury, England, 2006 (abstract published only).

20. N. Stepenosky, D. Green, J. Kounios, C. Clark and R. Polikar, “Majority Vote and decision template based ensemble classifiers trained on event related potentials for early diagnosis of Alzheimer’s disease,” IEEE Int. Conf. on Acoustic, Speech and Signal Processing (ICASSP 2006), vol. 5, pp. 901-904, Toulouse, France, May 2006.

21. H. Syed-Mohammed, N. Stepenosky and R. Polikar, “An Ensemble Technique to Handle Missing Data from Sensors,” IEEE Sensor Applications Symposium, pp.101-105, Houston, TX, February 2006.

22. R. Polikar, D. Parikh and S. Mandayam, “Multiple Classifier Systems for Multisensor Data Fusion, “IEEE Sensor Applications Symposium, pp. 180-184, Houston, TX, February 2006.

23. Clark C.M., Topalis A., Green D.L., Stepenosky N., Gandhi H., McCoubrey H., Kounios J., Xie S.and Polikar R., “Auditory event related potentials: A candidate physiologic biomarker for early detection of neurodegeneration associated with Alzheimer disease,” Alzheimer’s and Dementia, vol.2, no.3, supplement 1, pp. S141, 2006 (abstract published only).

24. Jansson P., Tang Y., Ramachandran R., Schmalzel J., Mandayam S., Krchnavek R., Head L., Polikar R., Ordonez R. “The role of engineering clinic in promoting an agile ECE learning environment,” Proc. ASEE Annual Conf. & Expo., Session 2432, , Chicago, IL, June 2006.

25. Jahan K., Chen J., Kadlowec J., Krchnavek R., Mandayam S., Mehta Y., Polikar R., Sukumaran B., Von Lockette P., “Digital imaging experiences for engineering students,” Proc. ASEE Annual Conf. & Expo., Session 1526, Chicago, IL, June 2006.

26. Parikh D., Stepenosky N. , Topalis A., Green D., Kounios J. , Clark C. and Polikar R. , “Ensemble based data fusion for
early diagnosis of Alzheimer’s disease,” Proc. of 27th Int. Conf. of IEEE Engineering in Medicine and Biology Soc., pp. 2749-2482, Shanghai, China, September 2005.

27. Stepenosky N., Topalis A., Syed H., Green D., Kounios J. , Clark C. and Polikar R. “Boosting based classification of event related potentials for early diagnosis of Alzheimer’s disease,” Proc. of 27th Int. Conf. of IEEE Engineering in Medicine and Biology Soc., pp. 2494-2497, Shanghai, China, September 2005.

28. Gangardiwala A. and Polikar R. , “Dynamically weighted majority voting for incremental learning and comparison of three boosting based approaches,” Proc. of Int. Joint Conf. on Neural Networks (IJCNN 2005), pp. 1131-1136, Montreal, QB, Canada, July y2005.

29. Parikh D. and Polikar R. , “A Multiple Classifier Approach for Multisensor Data Fusion,” Proc. of IEEE FUSION 2005, vol. 1, pp: 453-460, Philadelphia, PA July 2005.

30. Stepenosky N., Topalis A., Frymiare J., Kounios J Clark C. and Polikar R., “Comparison Of Pz, Fz And Cz Event Related Potentials For The Early Diagnosis Of Alzheimer's Disease,” ASME Summer Bioengineering Conference, Vail, CO, June 2005. (best student paper award, 2nd place).

31. Jacques G., Frymiare J., Kounios J., Clark C., Polikar R., “Multiresolution wavelet analysis and ensemble of classifiers for early diagnosis of Alzheimer’s disease,” Proc. of 30th IEEE Int. Conf. on Acoustics, Speech and Signal Processing (ICASSP 2005), vol. 5, pp. 389-392, Philadelphia, PA, March 2005.

32. Topalis A., Stepenosky N., Frymiare J., Kounios J., Clark C. and Polikar R., “Comparison of ERP spectral bands for early diagnosis of Alzheimer Disease using multiresolution wavelet analysis,” Alzheimer’s and Dementia, vol.1, no.1, supplement 1, pp. 23, 2005 (abstract only).

33. Polikar R., Head L., Ramachandran R. and Tahamont M., Integrating BME into ECE curriculum: an alternate approach for meeting the nation's need for qualified BME professionals,” ASEE Annual Conference and Exposition, Session 1526, pp. 8305-8322, Portland, OR, June 2005.

34. Polikar R. and Healy B., “A two-tiered classification algorithm for identification of binary mixtures of VOCs,” Proc. of 11th Int. Symp. on Olfaction and Electronic Nose (ISOEN2005), pp. 89-92, Barcelona, Spain, April 2005.

35. Parikh D., Kim M., Oagaro J., Mandayam S. and Polikar R., “Combining classifiers for multisensor data fusion,” Proc. of Int. IEEE Conf. on System Man Cybernetics (SMC 04), pp. 1232-1237, The Hague, The Netherlands, October 2004.

36. Jacques G., Frymiare J., Kounios J., Clark C., Polikar R., “Multiresolution analysis for early diagnosis of Alzheimer’s disease,” Proc. of 26th Annual Int. Conf. of IEEE Engineering in Medicine and Biology Soc. (EMBS2004), pp. 251-254, San Francisco, CA, Sept 2004.

37. Parikh D., Kim M., Oagaro J., Mandayam S. and Polikar R., “Ensemble of classifiers approach for NDE data fusion,” Proc. of 2004 IEEE Int. Ultrasonics, Ferroelectrics and Frequency Control Joint Conf (UFFC2004), vol. 2, pp. 1062-1065, Montreal, Canada, August 2004.

38. Muhlbaier M., Topalis A., Polikar R., “Incremental learning from unbalanced data,” Proc. of Int. Joint Conference on Neural Networks (IJCNN 2004), pp. 1057-1062, Budapest, Hungary, July 2004.

39. Polikar R., Ramachandran R., Head L., Tahamont M., “Biomedical engineering for all electrical engineers,” ASEE Annual Conference and Exposition, Session 1526, pp. 1193-1206, Salt Lake City, UT, June 2004.

40. Jahan K., et al. and Polikar R., “Digital imaging across engineering curriculum,” ASEE Annual Conference and Exposition, Session 2756, pp. 3879-3884, Salt Lake City, UT, June 2004.

41. Schmalzel J., Figueroa, F., Morris, J., Mandayam, S.; Polikar, R., “An architecture for intelligent systems based on smart sensors,” IEEE Instrumentation and Measurement Technology Conference (IMTC’04), vol. 1, pp 71-75, May 2004.

42. Figueroa, F.; Schmalzel, J.; Morris, J.; Solano, W.; Mandayam, S.; Polikar, R., “A framework for intelligent rocket test facilities with smart sensor elements,” Proc. of ISA/IEEE Sensors for Industry Conf., pp. 91-95, June 2004.

43. Papson S., Oagaro J., Polikar R., Chen J.C., Schmalzel J., Mandayam S., “A virtual reality environment for multi-sensor data integration,” Proc. of ISA/IEEE Sensors for Industry Conf., pp. 116-122, June 2004.

44. Polikar R., Krause S., Burd L., “Dynamic weight update in weighted majority voting for Learn++,” Proc. of Int. Joint Conference on Neural Networks (IJCNN 2003), vol. 4, pp. 2770-2775, Portland, OR, 20-24 July 2003.

45. Krause S. and Polikar R., An ensemble of classifiers for the missing feature problem,” Proc. of Int. Joint Conference on Neural Networks (IJCNN 2003), vol. 1, pp. 553-558, Portland, OR, 20-24 July 2003.

46. Polikar R. and Mandayam S. “ Machine learning and pattern recognition education at a non-Ph.D. granting engineering program, opportunities and challenges,” Int. Conf. on Art. Neural Networks (ICANN 2003), Istanbul, Turkey, 26-29 June 2003.

47. Jahan, K., Everett J.W., Orlins J., Hesketh, R.P., Farrell, S., Mehta Y., Hollar K., Polikar R. and Savelski M. “Research experiences in pollution prevention,” Proc. ASEE Annual Conference, Nashville, TN, July 2003.

48. Eckerd R., Neyhart J., Polikar R., Mandayam S., Tseng M., “A Modified Neyman-Pearson Technique for Radiodense Tissue Estimation in Digitized Mammograms,” Int. Conf. of 24th Annual IEEE Eng. in Medicine & Biology Soc. and Annual Fall Meeting of Biomedical Eng. Soc. EMBS/BMES 2002, vol. 2, pp. 995-996, October 2002, Houston, TX.

49. Schmalzel J.L., Mandayam S.A., Ramachandran R.P., Krchnavek R., Head L., Polikar R., Jansson P., and Ordonez R., “Continuous development of a New ECE Program,” Proc. of 2002 American Society for Engineering Education Annual Conference and Exposition, Session 2532, Montreal, Canada, 16-19 June 2002.

50. Polikar R., Byorick J., Krause S., Marino A., Moreton M., “Learn++: A classifier independent incremental learning algorithm for supervised neural networks,” Proc. of Int. Joint Conference on Neural Networks (IJCNN 2002), vol. 2, pp. 1742-1747, Honolulu, HI, 12-17 May 2002.

51. J. Byorick, R. Ramachandran, R. Polikar, “Isolated vowel recognition using linear predictive features and neural network classifier fusion,” Proc. of 5th ISIF/IEEE Int. Conf. on Information Fusion, vol. 2, pp. 1565-1572, Annapolis, MD, 8 – 11 July 2002.

52. T. Anastasia, G. Maenza, R. Polikar, “Wavelet Packets as a Means of Searching for Weak Narrow Band Signals,” Proc. of 4th IASTED Int. Conf. on Signal and Image Processing, Kauai, HI, 12-14 August 2002 .

53. Polikar R., “Learn++: An incremental learning algorithm based on psycho-physiological models of learning,” Proc. of 23rd Annual Int. Conf. of IEEE Engineering in Medicine and Biology Society (EMBS 2001), vol. 1, pp. 672-675, Istanbul, Turkey, 23-27 October 2001.

54. Neyhart J.T., Ciocco M.D., Polikar R., Mandayam S., Tseng M., “Dynamic segmentation of breast tissue in digitized mammograms,” Proc. of 23rd Annual Int. Conf. of IEEE Engineering in Medicine and Biology Society (EMBS 2001), vol. 3, pp. 2669-2672, Istanbul, Turkey, 23-27 October 2001.

55. Polikar R., Shinar R., Honavar V., Udpa L., Porter M.D., Detection and identification of odorants using an electronic nose, Proc. of IEEE 26th Int. Conf. on Acoustics, Speech and Signal Proc.(ICASSP 2001), vol. 5, pp. 3137-3140, Salt Lake City, UT, 7-11 May 2001.

56. Afzal M., Polikar R., Udpa L., Udpa S.S., Adaptive noise cancellation schemes for magnetic flux leakage signals obtained from gas pipeline inspection, Proc. of IEEE 26th Int. Conf. On Acoustics, Speech and Signal Processing (ICASSP 2001), vol. 6, pp. 3389-3392, Salt Lake City, UT, 2001.

57. Cai X., Ramuhalli P., Polikar R., Udpa L., Udpa S.S., Blind deconvolution for characterization of rotating probe eddy current data, Proc. of 10th Int. Symposium on Applied Electromagnetics and Mechanics (ISEM 2001), Tokyo, Japan, 2001.

58. Shekhar H., Polikar R., Ramuhalli P., Liu X., Das M., Udpa L., Udpa S.S., Dynamic thresholding for automated analysis and classification of bobbin probe eddy current data, Proc. of 10th Int. Symposium on Applied Electromagnetics and Mechanics (ISEM 2001), Tokyo, Japan, 13-16 May 2001.

59. Simone G., Morabito F., Polikar R., Ramuhalli P., Udpa L., Udpa, S.S., Feature extraction techniques for ultrasonic signal classification, Proc. of 10th Int. Symposium on Applied Electromagnetics and Mechanics (ISEM 2001), Tokyo, Japan, 13-16 May 2001.

60. Schmalzel J.L., Mandayam S.A., Ramachandran R.P., Krchnavek R.R., Head L., Ordonez R., Polikar R., Jansson P, Tracey J.H., Composing a new ECE program: the first five years, Proc. of 31st ASEE/IEEE Frontiers in Education Conf. (FIE 2001), Reno, NV, 10-13 October 2001.

61. Mandayam S.A., Schmalzel J.L., Ramachandran R.P., Krchnavek R.R., Head L., Ordonez R., Jansson P, and Polikar R., Assessment strategies: feedback is too late!, Proc. of 31st ASEE/IEEE Frontiers in Education Conference (FIE 2001), Reno, NV, 10-13 October 2001.

62. Polikar R., Udpa L., Udpa S., Honavar V., Learn++: An incremental learning algorithm for multilayer perceptrons, Proc. of IEEE 25th Int. Conf. On Acoustics, Speech and Signal Processing (ICASSP 2000), vol. 6, pp. 3414-3417, Istanbul, Turkey, 2000.

63. Ramuhalli, P., Polikar, R., Udpa L., Udpa S., Fuzzy ARTMAP network with evolutionary learning, Proc. of IEEE 25th Int. Conf. On Acoustics, Speech and Signal Processing (ICASSP 2000), vol. 6, pp. 3466-3469, Istanbul, Turkey, 2000.

64. Udpa L., Polikar R., Ramuhalli R., Udpa S.S., Spanner J., Development of an ultrasonic data acquisition and processing system, Proc. of 2nd Int. Conf. on NDE in Relation to Structural Integrity for Nuclear and Pressurized Component, New Orleans, LA, 2000.

65. Polikar R., Udpa L., Udpa S., Nonlinear cluster transformations for increasing pattern separability, Proc. of Int. Joint Conf. on Neural Networks (IJCNN’99), vol. 6, p. 4006-4011, Washington D.C., 1999.

66. Polikar R., Greer M., Udpa L., Keinert F., Multiresolution wavelet analysis of ERPs for the detection of Alzheimer's disease, Proc. of the IEEE 19th Int. Conf. of Engineering in Medicine and Biology Society , pp. 1301-1304, Chicago IL, 1997.

Other

1. Making Wavelets, Robi Polikar’s “The Wavelet Tutorial” featured by the Science Magazine’s NetWatch Department, Science, vol. 300, no. 561, pp. 873, May 2003.

Invited Talks & Special Presentations

1. Ensemble of Classifiers Approaches for Data Fusion, Incremental Learning, Learning in Nonstationary Environments and Becoming a Millionaire!, Electrical and Computer Engineering, Temple University, Philadelphia, PA—November 2008.

2. Learning in Nonstationary Environments– Drexel University, Computer Science & Electrical and Computer Eng. Department, Philadelphia, PA – February 11 & 25, 2008.

3. Ensemble Systems and Applications for Early Diagnosis of Alzheimer’s Disease, Stevens Institute of Technology, Hoboken, NJ , invited talk on – November 2007.

4. Ensemble Based Systems in Decision Making, Int. Joint Conf. on Neural Networks, Tutorial, Orlando, FL – August 2007

5. Ensemble Based Systems in Decision Making, Int. Conf. on Artificial Neural Networks, Tutorial, Athens, Greece – September 2006.

6. Ensemble Classifiers and Applications to Biomedical Engineering, Drexel University, School of Biomedical Engineering, invited talk on – November 2005

7. Wavelet Analysis for Early Diagnosis of Alzheimer’s Disease, University of Sciences in Philadelphia, invited talk – October 2005

8. Ensemble based systems for data fusion, Tutorial given at IEEE/ISIF Information Fusion Conference, Philadelphia, PA, 2005.

9. Neural networks and ensemble of classifiers for water quality monitoring, Invited Talk, New Jersey Department of Environmental Protection, Trenton, NJ, May 2004.

10. Ensemble of classifiers, Invited Talk, Lockheed Martin Co. Moorestown, NJ, March 2004.

11. Pattern Recognition and Neural Networks for Water Quality Assessment (1.2MB), invited talk presented by R. Polikar and M. Lewitt at Passaic Valley Water Commission, Totowa, NJ, May 23, 2003.

12. An Ensemble of Classifiers Approach for the Missing Feature Problem Using learn⁺⁺ (650K) , presented by Stefan Krause, at IEEE Region 2 Student Paper Contest, University of Maryland Eastern Shore, April 5, 2003. (First place)

13. Towards the Electronic Nose—Emerging Interdisciplinary Challenges (3MB), invited talk presented by R. Polikar at South Jersey Water Environment Association Meeting, Rowan University, Glassboro, NJ, October 2002

14. The Story of Wavelets (2MB), invited talk presented by R. Polikar at Rutgers University, New Brunswick, NJ, April 2002.

Last updated July 2009