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» Predicting Nucleolar Proteins Using Support-Vector Machines
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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
BICOB
2010
Springer
13 years 5 months ago
Multiple Kernel Learning for Fold Recognition
Fold recognition is a key problem in computational biology that involves classifying protein sharing structural similarities into classes commonly known as "folds". Rece...
Huzefa Rangwala
VIP
2003
13 years 8 months ago
Optimal Selection of Image Segmentation Algorithms Based on Performance Prediction
Using different algorithms to segment different images is a quite straightforward strategy for automated image segmentation. But the difficulty of the optimal algorithm selection ...
Yong Xia, David Dagan Feng, Rongchun Zhao
NIPS
2007
13 years 8 months ago
Multi-Task Learning via Conic Programming
When we have several related tasks, solving them simultaneously is shown to be more effective than solving them individually. This approach is called multi-task learning (MTL) and...
Tsuyoshi Kato, Hisashi Kashima, Masashi Sugiyama, ...
AUSAI
2008
Springer
13 years 9 months ago
Learning to Find Relevant Biological Articles without Negative Training Examples
Classifiers are traditionally learned using sets of positive and negative training examples. However, often a classifier is required, but for training only an incomplete set of pos...
Keith Noto, Milton H. Saier Jr., Charles Elkan