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» Optimal feature selection for support vector machines
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KDD
2004
ACM
166views Data Mining» more  KDD 2004»
14 years 8 months ago
Predicting prostate cancer recurrence via maximizing the concordance index
In order to effectively use machine learning algorithms, e.g., neural networks, for the analysis of survival data, the correct treatment of censored data is crucial. The concordan...
Lian Yan, David Verbel, Olivier Saidi
JCDL
2009
ACM
139views Education» more  JCDL 2009»
14 years 2 months ago
Topic model methods for automatically identifying out-of-scope resources
Recent years have seen the rise of subject-themed digital libraries, such as the NSDL pathways and the Digital Library for Earth System Education (DLESE). These libraries often ne...
Steven Bethard, Soumya Ghosh, James H. Martin, Tam...
ICASSP
2011
IEEE
12 years 11 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...
CIVR
2009
Springer
132views Image Analysis» more  CIVR 2009»
14 years 2 months ago
Real-time bag of words, approximately
We start from the state-of-the-art Bag of Words pipeline that in the 2008 benchmarks of TRECvid and PASCAL yielded the best performance scores. We have contributed to that pipelin...
Jasper R. R. Uijlings, Arnold W. M. Smeulders, Rem...
AMFG
2003
IEEE
152views Biometrics» more  AMFG 2003»
14 years 29 days ago
Fully Automatic Upper Facial Action Recognition
This paper provides a new fully automatic framework to analyze facial action units, the fundamental building blocks of facial expression enumerated in Paul Ekman’s Facial Action...
Ashish Kapoor, Yuan (Alan) Qi, Rosalind W. Picard