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» Boosting Kernel Models for Regression
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KDD
2005
ACM
168views Data Mining» more  KDD 2005»
14 years 7 months ago
Nomograms for visualizing support vector machines
We propose a simple yet potentially very effective way of visualizing trained support vector machines. Nomograms are an established model visualization technique that can graphica...
Aleks Jakulin, Martin Mozina, Janez Demsar, Ivan B...
AAAI
2010
13 years 7 months ago
Fast Conditional Density Estimation for Quantitative Structure-Activity Relationships
Many methods for quantitative structure-activity relationships (QSARs) deliver point estimates only, without quantifying the uncertainty inherent in the prediction. One way to qua...
Fabian Buchwald, Tobias Girschick, Eibe Frank, Ste...
ICML
2010
IEEE
13 years 8 months ago
Projection Penalties: Dimension Reduction without Loss
Dimension reduction is popular for learning predictive models in high-dimensional spaces. It can highlight the relevant part of the feature space and avoid the curse of dimensiona...
Yi Zhang 0010, Jeff Schneider
CIKM
2008
Springer
13 years 9 months ago
Trada: tree based ranking function adaptation
Machine Learned Ranking approaches have shown successes in web search engines. With the increasing demands on developing effective ranking functions for different search domains, ...
Keke Chen, Rongqing Lu, C. K. Wong, Gordon Sun, La...
AAAI
2010
13 years 8 months ago
G-Optimal Design with Laplacian Regularization
In many real world applications, labeled data are usually expensive to get, while there may be a large amount of unlabeled data. To reduce the labeling cost, active learning attem...
Chun Chen, Zhengguang Chen, Jiajun Bu, Can Wang, L...