Rajesh Girish Parekh

Publications

Journal

1. Kohavi, R., Mason, L., Parekh, R., and Zheng, Z., Lessons and Challenges from Mining Retail E-Commerce Data. Machine Learning vol 57 (1-2), pp. 83-113, 2004. [draft]
2. Parekh, R., and Honavar, V., Learning DFA from Simple Examples. Machine Learning vol 44, pp. 9-35, 2001.
   
3. Parekh, R., and Yang, J., and Honavar, V. Constructive Neural Network Learning Algorithms for Pattern Classification. IEEE Transactions on Neural Networks. 11(2), pp. 436-451. 2000.
4. Parekh, R., Yang, J., and Honavar, V., Comparison of Performance of Variants of Single-Layer Perceptron Algorithms on Non-separable Datasets. Neural, Parallel, and Scientific Computation, vol. 8, pp. 415-438, 2000.
   
5. Yang, J., Parekh, R., and Honavar, V., DistAl: An Inter-Pattern Distance-Based Constructive Learning Algorithm. Intelligent Data Analysis 3, pp. 55-73. 1999.

Invited Chapters

1. Parekh, R., and Honavar, V., Automata Induction, Grammar Inference, and Language Acquisition, The Handbook of Natural Language Processing. Dale, Moisl, and Somers (editors), Marcel Dekker Inc., New York, 2000. pp. 727-764.
2. Honavar, V., Yang, J., and Parekh, R., Constructive Learning and Structural Learning. Encyclopedia of Electrical and Electronics Engineering. Webster, J. (editor), New York: Wiley. 1999.

Refereed Conferences and Workshops

1. Kohavi, R., and Parekh, R., Visualizing RFM Segmentation. Proceedings of the 2004 SIAM International Conference on Data Mining (SDM'2004), Orlando, FL, pp. 391-399, April 22-24, 2004.
2. Kohavi, R., and Parekh, R., Ten Supplementary Analyses to Improve E-Commerce Websites. Proceedings of Webmining as a Premise to Effective and Intelligent Web Applications (WEBKDD'2003), Washington D.C., Aug. 27, 2003.
3. Parekh, R., and Honavar, V. On the Relationship between Models for Learning in Helpful Environments. Proceedings of the Fifth International Colloquium on Grammatical Inference (ICGI'2000), Lisbon, Portugal, pp. 207-220, Sep. 11-13, 2000.
4. Tuason, N., and Parekh, R. Mining Operational Databases to Predict Short-Term Defection Among Insured Households. Proceedings of the 2000 Advanced Research Techniques Forum (ART'2000), Monterey, CA. Jun 4-7, 2000.
5. Parekh, R., and Honavar, V. Simple DFA are Polynomially Probably Exactly Learnable from Simple Examples. Proceedings of the Sixteenth International Conference on Machine Learning (ICML’99), Bled, Slovenia, pp. 298-306, Jun. 27-30, 1999.
6. Yang, J., Parekh, R., Honavar, V., and Dobbs, D. Data-Driven Theory Refinement Using KBDistAl. Proceedings of the Third International Symposium on Intelligent Data Analysis (IDA’99). Amsterdam, The Netherlands, pp. 331-342, 1999.
7. Yang, J., Parekh, R., Honavar, V., and Dobbs, D. Data-Driven Theory Refinement Algorithms for Bioinformatics. Proceedings of the International Joint Conference on Neural Networks (IJCNN’99), Washington, D.C., vol. 6. pp. 4064-4068, 1999.
8. Parekh, R., Nichitiu, C., and Honavar, V., A Polynomial Time Incremental Algorithm for Learning DFA. Proceedings of the Fourth International Colloquium on Grammatical Inference (ICGI'98), Ames, IA. Lecture Notes in Computer Science vol. 1433 pp. 37-49, 1998.
9. Parekh, R., Yang, J., and Honavar, V., Constructive Theory Refinement in Knowledge Based Neural Networks. Proceedings of the IEEE/INNS International Joint Conference on Neural Networks (IJCNN'98), Anchorage, AK, pp. 2318-2323, May 4-8, 1998.
10. Yang, J., Parekh, R., and Honavar, V., DistAl: An Inter-Pattern Distance-Based Constructive Learning Algorithm. Proceedings of the IEEE/INNS International Joint Conference on Neural Networks (IJCNN'98), Anchorage, AK, pp. 2208-2213, May 4-8, 1998.
11. Parekh, R., and Honavar, V., Learning DFA from Simple Examples. Proceedings of the Eighth International Workshop on Algorithmic Learning Theory (ALT'97), Sendai, Japan. Oct. 6-8, 1997. Lecture Notes in Computer Science vol. 1316, pp. 116-131, 1997. A version of this paper was presented at the Workshop on Grammar Inference, Automata Induction, and Language Acquisition (ICML'97), Nashville, TN. Jul. 12, 1997.
12. Parekh, R., Yang, J., and Honavar, V., Pruning Strategies for the MTiling Constructive Learning Algorithm. Proceedings of the IEEE/INNS International Conference on Neural Networks (ICNN'97), Houston, TX, vol III, pp. 1960-1965, June 9-12, 1997.
13. Parekh, R., Yang, J., and Honavar, V., MUpstart - A Constructive Neural Network Learning Algorithm for Multi-Category Pattern Classification. Proceedings of the IEEE/INNS International Conference on Neural Networks (ICNN'97), Houston, TX, vol III, pp. 1924-1929, June 9-12, 1997.
14. Parekh, R., and Honavar, V., An Incremental Interactive Algorithm for Regular Grammar Inference. Proceedings of the Third International Colloquium on Grammatical Inference (ICGI'96), Montpellier, France. Lecture Notes in Computer Science vol. 1147, pp. 238-250, September 24-27, 1996.
15. Yang, J., Parekh, R., and Honavar, V., MTiling - A Constructive Neural Network Learning Algorithm for Multi-Category Pattern Classification. Proceedings of the WCNN'96, San Diego, CA, pp. 182-187, September 15-20, 1996.
16. Racherla, G., Killian, S., Fife, L., Lehmann, M., and Parekh, R., PARSIT: A parallel algorithm reconfiguration simulation tool. Proceedings of the 1995 International Conference on High Performance Computing, New Delhi, India, pp. 373-378, December 27-30, 1995.
17. Chen, C-H., Parekh, R., Yang, J., Balakrishnan, K., and Honavar, V., Analysis of Decision Boundaries Generated by Constructive Neural Learning Algorithms. Proceedings of the World Congress on Neural Networks (WCNN'95), Washington D.C., pp. 628-635, July 17-21, 1995.
18. Parekh, R., and Honavar, V., Efficient Learning of Regular Languages using Teacher Supplied Positive Samples and Learner Generated Queries. Proceedings of the Fifth UNB Conference on AI , Fredrickton, Canada, pp. 195-203, August 11-14, 1993.
19. Parekh R., Balakrishnan, K., and Honavar, V., An Empirical Comparison of Flat-Spot Elimination Techniques in Back Propagation Neural Networks. Proceedings of the SIMTEC/WNN'92, Clear Lake, TX, pp. 463-468, November 4-6, 1992.
20. Arunkumar, S., Lal, R., Venkatagopal, R., Parekh, R. and Daruwala, R., SIMPAC: A Simulator for Parallel Architectures and Computing. Proceedings of the IEEE Region 10 Conference, Tencon'92 Melbourne, Australia, pp. 196-200, 1992.

Ph.D. Dissertation

Parekh, R. Constructive Learning: Inducing Grammars and Neural Networks. Iowa State University, Ames, IA, 1998.

Patents

1. Parekh, R., Parikh, J., and Berkhin, P. Predicting Newsworthy Queries using Combined Online and Offline Models, Filed April 2008.
2. Parikh, J., Bhamidipati, N., and Parekh, R., Identifying Super-Phrases of Text Strings, Filed April 2008.
3. Parekh, R., Parmar, J. and Berkhin, P. Personalizing Sponsored Search Advertising Layout using User Behavior History, Filed March 2008.
4. Bhatt, R., Parikh, J., Parekh, R., and Berkhin, P. Differentiated Treatment of Sponsored Search Results Based on Search Context. Filed October 2007.
5. Tomkins, A., Ojjeh, B., Berkhin, P., Parekh, R., Gaffney, S., Fayyad, U. Bid Optimization in Search Engine Marketing. Filed December 2006.
6. Klots, B., Eneva, E., Mayoraz, N., Berkhin, P., Parekh, R., Chow, R. Evaluating the Performance of Binary Classification Systems. Filed December 2006.
7. Fayyad, U., Berkhin, P., Tomkins, A., Parekh, R., Parikh, J., Sculley, D. Keyword Set and Target Audience Profile Generalization Techniques. Filed May 2006.