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» Applying Support Vector Machines to Imbalanced Datasets
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BMCBI
2006
180views more  BMCBI 2006»
14 years 12 months ago
Building multiclass classifiers for remote homology detection and fold recognition
Motivation Protein remote homology prediction and fold recognition are central problems in computational biology. Supervised learning algorithms based on support vector machines a...
Huzefa Rangwala, George Karypis
IJON
2006
87views more  IJON 2006»
14 years 11 months ago
Translation-invariant classification of non-stationary signals
Non-stationary signal classification is a complex problem. This problem becomes even more difficult if we add the following hypothesis: each signal includes a discriminant wavefor...
Vincent Guigue, Alain Rakotomamonjy, Stépha...
JRTIP
2008
300views more  JRTIP 2008»
14 years 11 months ago
Real-time human action recognition on an embedded, reconfigurable video processing architecture
Abstract In recent years, automatic human action recognition has been widely researched within the computer vision and image processing communities. Here we propose a realtime, emb...
Hongying Meng, Michael Freeman, Nick Pears, Chris ...
MICCAI
2010
Springer
14 years 10 months ago
Brain Morphometry by Probabilistic Latent Semantic Analysis
The paper proposes a new shape morphometry approach to combine advanced classification techniques with geometric features in order to identify morphological abnormalities on brain...
Umberto Castellani, Alessandro Perina, Vittorio Mu...
TKDE
2010
168views more  TKDE 2010»
14 years 10 months ago
Completely Lazy Learning
—Local classifiers are sometimes called lazy learners because they do not train a classifier until presented with a test sample. However, such methods are generally not complet...
Eric K. Garcia, Sergey Feldman, Maya R. Gupta, San...