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» A PAC Bound for Approximate Support Vector Machines
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KDD
2004
ACM
166views Data Mining» more  KDD 2004»
14 years 7 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
VISUALIZATION
2005
IEEE
14 years 28 days ago
Illuminated Lines Revisited
For the rendering of vector and tensor fields, several texturebased volumetric rendering methods were presented in recent years. While they have indisputable merits, the classica...
Ovidio Mallo, Ronald Peikert, Christian Sigg, Fili...
CRV
2009
IEEE
115views Robotics» more  CRV 2009»
14 years 2 months ago
Learning Model Complexity in an Online Environment
In this paper we introduce the concept and method for adaptively tuning the model complexity in an online manner as more examples become available. Challenging classification pro...
Dan Levi, Shimon Ullman
NIPS
2003
13 years 8 months ago
Max-Margin Markov Networks
In typical classification tasks, we seek a function which assigns a label to a single object. Kernel-based approaches, such as support vector machines (SVMs), which maximize the ...
Benjamin Taskar, Carlos Guestrin, Daphne Koller
BMCBI
2004
176views more  BMCBI 2004»
13 years 7 months ago
Boosting accuracy of automated classification of fluorescence microscope images for location proteomics
Background: Detailed knowledge of the subcellular location of each expressed protein is critical to a full understanding of its function. Fluorescence microscopy, in combination w...
Kai Huang, Robert F. Murphy