@inbook{citeulike:2418199,
        title = {Relevance Feedback in Information Retrieval},
        address = {Englewood, Cliffs, New Jersey},
        author = {J. J. Rocchio},
        booktitle = {The SMART Retrieval System - Experiments in Automatic Document Processing},
        editor = {Gerard Salton},
        publisher = {Prentice Hall},
        year = 1971,
        url = {http://www.is.informatik.uni-duisburg.de/bib/docs/Rocchio\_71.html},
        id = {2418199},
	priority = {0},
	at = {2008-02-23 11:05:01},
	biburl = {http://www.bibsonomy.org/bibtex/2fe8a97fabe2ba57e118bf5585bd7cf03/pprett},
	keywords = {rocchio feedback, relevance,}
}

@inproceedings{citeulike:1369124,
        title = {Boosting and Rocchio applied to text filtering},
        address = {Melbourne, AU},
        author = {Robert E. Schapire and Yoram Singer and Amit Singhal},
        booktitle = {Proceedings of SIGIR-98, 21st ACM International Conference on Research and Development in Information Retrieval},
        editor = {Bruce W. Croft and Alistair Moffat and Cornelis J. van Rijsbergen and Ross Wilkinson and Justin Zobel},
        pages = {215--223},
        publisher = {ACM Press, New York, US},
        year = 1998,
        url = {http://citeseer.ist.psu.edu/schapire98boosting.html},
        id = {1369124},
	priority = {4},
	at = {2008-02-21 20:10:24},
	abstract = {We discuss two learning algorithms for text filtering: modified Rocchio and a boosting algorithm called AdaBoost. We show how both algorithms can be adapted to maximize any general utility matrix that associates cost (or gain) for each pair of machine prediction and correct label. We first show that AdaBoost significantly outperforms another highly effective text filtering algorithm. We then compare AdaBoost and Rocchio over three large text filtering tasks. Overall both algorithms are...},
	biburl = {http://www.bibsonomy.org/bibtex/2d1bcc20efbb28e33012cb22fc53b0787/pprett},
	keywords = {rocchio information, filtering, boosting,}
}

@incollection{citeulike:2011339,
        title = {Extending the Rocchio Relevance Feedback Algorithm to Provide Contextual Retrieval},
        author = {Chris Jordan and Carolyn Watters},
        journal = {Advances in Web Intelligence},
        pages = {135--144},
        year = 2004,
        url = {http://www.springerlink.com/content/cyd702mnbkhqpcjy
},
        id = {2011339},
	priority = {2},
	at = {2007-11-29 07:15:00},
	abstract = {Contextual retrieval supports differences amongst users in their information seeking requests. The Web, which is very dynamic and nearly universally accessible, is an environment in which it is increasingly difficult for users to find documents that satisfy their specific information needs. This problem is amplified as users tend to use short queries. Contextual retrieval attempts to address this problem by incorporating knowledge about the user and past retrieval results in the search process. In this paper we explore a feedback technique based on the Rocchio algorithm that significantly reduces demands on the user while maintaining comparable performance on the Reuters-21578 corpus.},
	biburl = {http://www.bibsonomy.org/bibtex/242467d5f1674ddd469f1b59b7687b89f/pprett},
	keywords = {contextual, rocchio feedback, relevance,}
}

@inproceedings{citeulike:1992458,
        title = {Incremental Relevance Feedback for Information Filtering},
        author = {James Allan},
        booktitle = {Research and Development in Information Retrieval},
        pages = {270--278},
        year = 1996,
        url = {http://citeseer.ist.psu.edu/allan96incremental.html},
        id = {1992458},
	priority = {2},
	at = {2007-11-27 11:19:38},
	abstract = {We use data from the TREC routing experiments to explore
how relevance feedback can be applied incrementally---using
a few judged documents each time---to achieve results that
are as good as if the feedback occurred in one pass. We
show that relatively few judgments are needed to get highquality
results. We also demonstrate methods that reduce
the amount of information archived from past judged documents
without adversely affecting effectiveness. A novel simulation
shows that such techniques...},
	biburl = {http://www.bibsonomy.org/bibtex/24b53d2f3ff9dc036ff274930406b0427/pprett},
	keywords = {rocchio filtering, relevance, feedback, information, incremental,}
}

@inproceedings{joachims1997rocchio,
        title = {A Probabilistic Analysis of the Rocchio Algorithm with TFIDF for Text Categorization.},
        author = {Thorsten Joachims},
        booktitle = {ICML},
        crossref = {conf/icml/1997},
        editor = {Douglas H. Fisher},
        pages = {143-151},
        publisher = {Morgan Kaufmann},
        year = 1997,
        url = {http://dblp.uni-trier.de/db/conf/icml/icml1997.html#Joachims97},
        isbn = {1-55860-486-3},
	date = {2002-12-04},
	biburl = {http://www.bibsonomy.org/bibtex/2dfa99d567392038673882c932153053c/jil},
	keywords = {rocchio probabilistic laplace estimator bayes tfidf}
}

@inproceedings{han2000rocchio,
        title = {Centroid-Based Document Classification: Analysis and Experimental Results.},
        author = {Eui-Hong Han and George Karypis},
        booktitle = {PKDD},
        crossref = {conf/pkdd/2000},
        editor = {Djamel A. Zighed and Henryk Jan Komorowski and Jan M. Zytkow},
        pages = {424-431},
        publisher = {Springer},
        series = {Lecture Notes in Computer Science},
        volume = 1910,
        year = 2000,
        url = {http://glaros.dtc.umn.edu/gkhome/fetch/papers/centroidPKDD00.pdf},
        ee = {http://link.springer.de/link/service/series/0558/bibs/1910/19100424.htm},
	isbn = {3-540-41066-X},
	date = {2002-07-22},
	biburl = {http://www.bibsonomy.org/bibtex/2e46f97a70e986c33b1822d6a247dd1a5/jil},
	keywords = {simple rocchio classifier tight loose machine classification cosinus learning similarity klassifikation interpretation cos average}
}

@inproceedings{buckley94effect,
        title = {The effect of adding relevance information in a relevance feedback environment},
        author = {C. Buckley and G. Salton and J. Allan},
        booktitle = {Proceedings of the seventeenth annual international {ACM}-{SIGIR} conference on research and development in information retrieval},
        publisher = {Springer-Verlag},
        year = 1994,
        url = {citeseer.ist.psu.edu/buckley94effect.html},
        biburl = {http://www.bibsonomy.org/bibtex/2f157419cdcae5333b2a36f2d3dea64c7/jil},
	keywords = {relevance gewichtung feedback weights rocchio gewichte}
}

@inproceedings{schapire98rocchio,
        title = {Boosting and Rocchio applied to text filtering.},
        address = {Melbourne, Australia},
        author = {Robert E. Schapire and Yoram Singer and Amit Singhal},
        booktitle = {Proceedings of {SIGIR}-98, 21st {ACM} International Conference on
	Research and Development in Information Retrieval},
        pages = {215--223},
        publisher = {ACM Press, New York, US},
        year = 1998,
        url = {http://singhal.info/rocboost.pdf},
        biburl = {http://www.bibsonomy.org/bibtex/2711d31d265daf4bb9c3453f8a87727c0/jil},
	keywords = {rocchio machine classifier classification klassifikator shapire learning}
}

@inproceedings{lewis1995,
        title = {Text Categorization of Low Quality Images},
        author = {David J. Ittner and David D. Lewis and David D. Ahn},
        pages = {301-315},
        year = 1995,
        url = {http://staff.science.uva.nl/~ahn/pub/sdair.pdf},
        biburl = {http://www.bibsonomy.org/bibtex/214c1324528d8b10a4434c05a7446a7c7/jil},
	keywords = {rocchio klassifikator machine classification learning classifier}
}