Evolving Strategies for Focused Web Crawling

Proceedings, Twentieth International Conference on Machine Learning

Volumn 1, Issue , 2003, Pages 298-305

  Johnson, Judy ^a   Tsioutsiouliklis, Kostas ^a   Lee Giles, C ^b  

^a NEC LABORATORIES AMERICA   (United States)

^b PENNSYLVANIA STATE UNIVERSITY   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

COMPUTER SIMULATION; DATABASE SYSTEMS; GENETIC ALGORITHMS; INDEXING (OF INFORMATION); LEARNING SYSTEMS; SEARCH ENGINES;

RANK FUNCTION; UNIFORM RESOURCE LOCATOR;

WORLD WIDE WEB;

EID: 1942484949     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (16)

1. Chakrabarti, S., Punera, K., & Subramanyam, M. (2002). Accelerated focused crawling through online relevance feedback. WWW, Hawaii. ACM.
2. Chakrabarti, S., van den Berg, M., & Dom, B. (1999). Focused crawling: a new approach to topic-specific Web resource discovery. Computer Networks (Amsterdam, Netherlands: 1999), 31, 1623-1640.
3. Diligenti, M., Coetzee, F., Lawrence, S., Giles, C. L., & Gori, M. (2000). Focused crawling using context graphs. 26th International Conference on Very Large Databases, VLDB 2000 (pp. 527-534). Cairo, Egypt.
4. Drucker, H., Shahrary, B., & Gibbon, D. C. (2001). Relevance feedback using support vector machines. Proc. 18th International Conf. on Machine Learning (pp. 122-129). Morgan Kaufmann, San Francisco, CA.
5. Eshelman, L. J. (1991). The chc adaptive search algorithm: how to have safe search when. engaging in nontraditional genetic recombination. In G. J. E. Rawlins (Ed.), Foundations of genetic algorithms, 265-283. Los Altos, CA: Morgan Kaufmann Publishers.
6. Flake, G., Lawrence, S., & Giles, C. L. (2000). Efficient identification of web communities. Sixth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (pp. 150-160). Boston, MA.
7. Gibson, D., Kleinberg, J. M., & Raghavan, P. (1998). Inferring web communities from link topology. UK Conference on Hypertext (pp. 225-234).
8. Goldberg, D. (1989). Genetic algorithms in search, optimization and machine learning. Reading, MA: Addison-Wesley. google (2003). http://www.google.com/.
9. Holland, J. (1975). Adaption in natural and artificial systems. Ann Arbor, MI: University of Michigan Press.
10. Joachims, T. (1998). Text categorization with support vector machines: learning with many relevant features. Proceedings of ECML-98, 10th European Conference on Machine Learning (pp. 137-142). Chemnitz, DE: Springer Verlag, Heidelberg, DE.
11. Kleinberg, J. M. (1999). Authoritative sources in a hyperlinked environment. Journal of the ACM, 46, 604-632.
12. Louis, S. J., & Johnson, J. (1997). Solving similar problems using genetic algorithms and case-based memory. Proceedings of the Seventh International Conference on Genetic Algorithms (ICGA97). San Francisco, CA: Morgan Kaufmann.
13. Menczer, F., Pant, G., & Srinivasan, P. (2003). Topical web crawlers: Evaluating adaptive algorithms. http://dollar.biz.uiowa.edu/~fil/Papers/TOIT. pdf.
14. Menczer, F., Pant, G., Srinivasan, P., & Ruiz, M. E. (2001). Evaluating topic-driven web crawlers. Research and Development in Information Retrieval (pp. 241-249).
15. Pant, G., & Menczer, F. (2002). Myspiders: Evolve your own intelligent web crawlers. Autonomous Agents and Multi-Agent Systems, 5, 241-249.
16. Srinivasan, P., Menczer, F., & Pant, G. (2003). A general evaluation framework for topical crawlers. Information Retrieval, Submitted.