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» Predicting Nucleolar Proteins Using Support-Vector Machines
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BMCBI
2004
176views more  BMCBI 2004»
14 years 9 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
14 years 7 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
14 years 10 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
14 years 11 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, ...
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AUSAI
2008
Springer
14 years 11 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