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» Covering Numbers for Support Vector Machines
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75
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CI
2006
101views more  CI 2006»
14 years 9 months ago
Sentiment Classification of Movie Reviews Using Contextual Valence Shifters
We present two methods for determining the sentiment expressed by a movie review. The semantic orientation of a review can be positive, negative, or neutral. We examine the effect...
Alistair Kennedy, Diana Inkpen
KDD
2005
ACM
130views Data Mining» more  KDD 2005»
15 years 10 months ago
Simple and effective visual models for gene expression cancer diagnostics
In the paper we show that diagnostic classes in cancer gene expression data sets, which most often include thousands of features (genes), may be effectively separated with simple ...
Gregor Leban, Minca Mramor, Ivan Bratko, Blaz Zupa...
84
Voted
BMCBI
2010
134views more  BMCBI 2010»
14 years 9 months ago
Semi-automated screening of biomedical citations for systematic reviews
Background: Systematic reviews address a specific clinical question by unbiasedly assessing and analyzing the pertinent literature. Citation screening is a time-consuming and crit...
Byron C. Wallace, Thomas A. Trikalinos, Joseph Lau...
96
Voted
ICASSP
2011
IEEE
14 years 1 months ago
Online Kernel SVM for real-time fMRI brain state prediction
The Support Vector Machine (SVM) methodology is an effective, supervised, machine learning method that gives stateof-the-art performance for brain state classification from funct...
Yongxin Taylor Xi, Hao Xu, Ray Lee, Peter J. Ramad...
KDD
2006
ACM
272views Data Mining» more  KDD 2006»
15 years 10 months ago
YALE: rapid prototyping for complex data mining tasks
KDD is a complex and demanding task. While a large number of methods has been established for numerous problems, many challenges remain to be solved. New tasks emerge requiring th...
Ingo Mierswa, Michael Wurst, Ralf Klinkenberg, Mar...