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106
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ECML
2004
Springer
15 years 7 months ago
Improving Random Forests
Random forests are one of the most successful ensemble methods which exhibits performance on the level of boosting and support vector machines. The method is fast, robust to noise,...
Marko Robnik-Sikonja
137
Voted
CVPR
2006
IEEE
15 years 6 months ago
Region-based Image Annotation using Asymmetrical Support Vector Machine-based Multiple-Instance Learning
In region-based image annotation, keywords are usually associated with images instead of individual regions in the training data set. This poses a major challenge for any learning...
Changbo Yang, Ming Dong, Jing Hua
144
Voted
CVPR
2012
IEEE
13 years 4 months ago
Robust Boltzmann Machines for recognition and denoising
While Boltzmann Machines have been successful at unsupervised learning and density modeling of images and speech data, they can be very sensitive to noise in the data. In this pap...
Yichuan Tang, Ruslan Salakhutdinov, Geoffrey E. Hi...
ICML
2005
IEEE
16 years 3 months ago
Unsupervised evidence integration
Many biological propositions can be supported by a variety of different types of evidence. It is often useful to collect together large numbers of such propositions, together with...
Philip M. Long, Vinay Varadan, Sarah Gilman, Mark ...
GFKL
2007
Springer
152views Data Mining» more  GFKL 2007»
15 years 8 months ago
Supporting Web-based Address Extraction with Unsupervised Tagging
Abstract. The manual acquisition and modeling of tourist information as e.g. addresses of points of interest is time and, therefore, cost intensive. Furthermore, the encoded inform...
Berenike Loos, Chris Biemann