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» Evaluating learning algorithms and classifiers
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SIGIR
2002
ACM
13 years 10 months ago
Unsupervised document classification using sequential information maximization
We present a novel sequential clustering algorithm which is motivated by the Information Bottleneck (IB) method. In contrast to the agglomerative IB algorithm, the new sequential ...
Noam Slonim, Nir Friedman, Naftali Tishby
SGAI
2010
Springer
13 years 8 months ago
Induction of Modular Classification Rules: Using Jmax-pruning
The Prism family of algorithms induces modular classification rules which, in contrast to decision tree induction algorithms, do not necessarily fit together into a decision tree s...
Frederic T. Stahl, Max Bramer
WWW
2009
ACM
14 years 11 months ago
Web-scale classification with naive bayes
Traditional Naive Bayes Classifier performs miserably on web-scale taxonomies. In this paper, we investigate the reasons behind such bad performance. We discover that the low perf...
Congle Zhang, Gui-Rong Xue, Yong Yu, Hongyuan Zha
WWW
2008
ACM
14 years 11 months ago
Behavioral classification on the click graph
A bipartite query-URL graph, where an edge indicates that a document was clicked for a query, is a useful construct for finding groups of related queries and URLs. Here we use thi...
Martin Szummer, Nick Craswell
ICASSP
2009
IEEE
14 years 5 months ago
Cultural style based music classification of audio signals
Music classification based on cultural style is useful for music analysis and has potential applications in retrieval and recommendation systems. In this paper, we present the fir...
Yuxiang Liu, Qiaoliang Xiang, Ye Wang, Lianhong Ca...