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» Evaluating learning algorithms and classifiers
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DAGM
2010
Springer
15 years 5 months ago
On-Line Multi-view Forests for Tracking
Abstract. A successful approach to tracking is to on-line learn discriminative classifiers for the target objects. Although these trackingby-detection approaches are usually fast a...
Christian Leistner, Martin Godec, Amir Saffari, Ho...
149
Voted
BIBM
2010
IEEE
139views Bioinformatics» more  BIBM 2010»
15 years 2 months ago
Scalable, updatable predictive models for sequence data
The emergence of data rich domains has led to an exponential growth in the size and number of data repositories, offering exciting opportunities to learn from the data using machin...
Neeraj Koul, Ngot Bui, Vasant Honavar
MICCAI
2009
Springer
16 years 5 months ago
MKL for Robust Multi-modality AD Classification
We study the problem of classifying mild Alzheimer's disease (AD) subjects from healthy individuals (controls) using multi-modal image data, to facilitate early identification...
Chris Hinrichs, Vikas Singh, Guofan Xu, Sterlin...
AUSDM
2008
Springer
225views Data Mining» more  AUSDM 2008»
15 years 6 months ago
Evaluation of Malware clustering based on its dynamic behaviour
Malware detection is an important problem today. New malware appears every day and in order to be able to detect it, it is important to recognize families of existing malware. Dat...
Ibai Gurrutxaga, Olatz Arbelaitz, Jesús M. ...
TSP
2010
14 years 11 months ago
Learning graphical models for hypothesis testing and classification
Sparse graphical models have proven to be a flexible class of multivariate probability models for approximating high-dimensional distributions. In this paper, we propose techniques...
Vincent Y. F. Tan, Sujay Sanghavi, John W. Fisher ...