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» On Pairwise Naive Bayes Classifiers
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SIGIR
2002
ACM
13 years 7 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
NAR
2007
91views more  NAR 2007»
13 years 7 months ago
RNABindR: a server for analyzing and predicting RNA-binding sites in proteins
Understanding interactions between proteins and RNA is key to deciphering the mechanisms of many important biological processes. Here we describe RNABindR, a web-based server that...
Michael Terribilini, Jeffry D. Sander, Jae-Hyung L...
IEAAIE
2010
Springer
13 years 5 months ago
Using Remote Data Mining on LIDAR and Imagery Fusion Data to Develop Land Cover Maps
Remote sensing based on imagery has traditionally been the main tool used to extract land uses and land cover (LULC) maps. However, more powerful tools are needed in order to fulfi...
Jorge García-Gutiérrez, Francisco Ma...
BMCBI
2005
251views more  BMCBI 2005»
13 years 7 months ago
Contextual weighting for Support Vector Machines in literature mining: an application to gene versus protein name disambiguation
Background: The ability to distinguish between genes and proteins is essential for understanding biological text. Support Vector Machines (SVMs) have been proven to be very effici...
Tapio Pahikkala, Filip Ginter, Jorma Boberg, Jouni...
ML
2000
ACM
124views Machine Learning» more  ML 2000»
13 years 7 months ago
Text Classification from Labeled and Unlabeled Documents using EM
This paper shows that the accuracy of learned text classifiers can be improved by augmenting a small number of labeled training documents with a large pool of unlabeled documents. ...
Kamal Nigam, Andrew McCallum, Sebastian Thrun, Tom...