Analysis of a very large web search engine query log
ACM SIGIR Forum
A statistical learning learning model of text classification for support vector machines
Proceedings of the 24th annual international ACM SIGIR conference on Research and development in information retrieval
Less is More: Active Learning with Support Vector Machines
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
Genre as noise: noise in genre
International Journal on Document Analysis and Recognition
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Even prior to content, the genre of a web document leads to a first coarse binary classification of the recall space in relevant and non-relevant documents. Thinking of a genre search engine, massive data will be available via explicit or implicit user feedback. These data can be used to improve and to customize the underlying classifiers. A taxonomy of user behaviors is applied to model different scenarios of information gain. Elements of such a learning interface, as for example the implications of the lingering time and the snippet genre recognition factor, are discussed.