| Alternate document: Details Using Reinforcement Learning to Spider the Web Efficiently (99) Jason Rennie, Andrew McCallum |
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Abstract: Consider the task of exploring the Web in order to find pages of a particular kind or on a particular topic. This task arises in the construction of search engines and Web knowledge bases. This paper argues that the creation of efficient web spiders is best framed and solved by reinforcement learning, a branch of machine learning that concerns itself with optimal sequential decision making. (Update)
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BibTeX entry: (Update)
Jason Rennie and Andrew McCallum. Using reinforcement learning to spider the Web efficiently. In ICML-99, 1999. http://citeseer.ist.psu.edu/rennie99using.html More
@inproceedings{ rennie99using,
author = "Jason Rennie and Andrew Kachites McCallum",
title = "Using reinforcement learning to spider the {W}eb efficiently",
booktitle = "Proceedings of {ICML}-99, 16th International Conference on Machine Learning",
publisher = "Morgan Kaufmann Publishers, San Francisco, US",
address = "Bled, SL",
editor = "Ivan Bratko and Saso Dzeroski",
pages = "335--343",
year = "1999",
url = "citeseer.ist.psu.edu/rennie99using.html" }
Citations (may not include all citations):
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Ecient crawling through URL ordering
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Regression using classi cation algorithms (context) - Torgo, Gama - 1997
The graph only includes citing articles where the year of publication is known.
Documents on the same site (http://www.ai.mit.edu/~jrennie/):
Building Domain-Specific Search Engines with Machine .. - McCallum, Nigam.. (1999)
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Using Reinforcement Learning to Spider the Web Efficiently - Rennie, McCallum (1999)
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