Difference between revisions of "Class meeting for 10-605 2012 02 02"
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=== Readings for the Class === | === Readings for the Class === | ||
| − | * | + | *[http://nlp.stanford.edu/IR-book/html/htmledition/irbook.html Introduction to Information Retrieval], by Christopher D. Manning, Prabhakar Raghavan & Hinrich Schütz, has a fairly [http://nlp.stanford.edu/IR-book/html/htmledition/scoring-term-weighting-and-the-vector-space-model-1.html self-contained chapter on the vector space model], including Rocchio's method. |
=== Also discussed === | === Also discussed === | ||
| + | * Joachims, Thorsten, [http://www.cs.cornell.edu/People/tj/publications/joachims_97a.pdf A Probabilistic Analysis of the Rocchio Algorithm with TFIDF for Text Categorization]. Proceedings of International Conference on Machine Learning (ICML), 1997. | ||
* Relevance Feedback in Information Retrieval, SMART Retrieval System Experiments in Automatic Document Processing, 1971, Prentice Hall Inc. | * Relevance Feedback in Information Retrieval, SMART Retrieval System Experiments in Automatic Document Processing, 1971, Prentice Hall Inc. | ||
* Schapire et al, [http://dl.acm.org/citation.cfm?id=290996 Boosting and Rocchio applied to text filtering], SIGIR 98. | * Schapire et al, [http://dl.acm.org/citation.cfm?id=290996 Boosting and Rocchio applied to text filtering], SIGIR 98. | ||
* Littlestone, [http://www.springerlink.com/index/X1022977778L1777.pdf Learning quickly when irrelevant attributes abound: A new linear-threshold algorithm], MLJ 1988. Includes the mistake-bound theory. | * Littlestone, [http://www.springerlink.com/index/X1022977778L1777.pdf Learning quickly when irrelevant attributes abound: A new linear-threshold algorithm], MLJ 1988. Includes the mistake-bound theory. | ||
Latest revision as of 18:07, 2 February 2012
This is one of the class meetings on the schedule for the course Machine Learning with Large Datasets 10-605 in Spring_2012.
Slides
Readings for the Class
- Introduction to Information Retrieval, by Christopher D. Manning, Prabhakar Raghavan & Hinrich Schütz, has a fairly self-contained chapter on the vector space model, including Rocchio's method.
Also discussed
- Joachims, Thorsten, A Probabilistic Analysis of the Rocchio Algorithm with TFIDF for Text Categorization. Proceedings of International Conference on Machine Learning (ICML), 1997.
- Relevance Feedback in Information Retrieval, SMART Retrieval System Experiments in Automatic Document Processing, 1971, Prentice Hall Inc.
- Schapire et al, Boosting and Rocchio applied to text filtering, SIGIR 98.
- Littlestone, Learning quickly when irrelevant attributes abound: A new linear-threshold algorithm, MLJ 1988. Includes the mistake-bound theory.
