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ECML
2004
Springer
14 years 2 months ago
Applying Support Vector Machines to Imbalanced Datasets
Support Vector Machines (SVM) have been extensively studied and have shown remarkable success in many applications. However the success of SVM is very limited when it is applied to...
Rehan Akbani, Stephen Kwek, Nathalie Japkowicz
IDEAL
2010
Springer
13 years 6 months ago
Robust 1-Norm Soft Margin Smooth Support Vector Machine
Based on studies and experiments on the loss term of SVMs, we argue that 1-norm measurement is better than 2-norm measurement for outlier resistance. Thus, we modify the previous 2...
Li-Jen Chien, Yuh-Jye Lee, Zhi-Peng Kao, Chih-Chen...
IWANN
2009
Springer
14 years 3 months ago
Feature Selection in Survival Least Squares Support Vector Machines with Maximal Variation Constraints
This work proposes the use of maximal variation analysis for feature selection within least squares support vector machines for survival analysis. Instead of selecting a subset of ...
Vanya Van Belle, Kristiaan Pelckmans, Johan A. K. ...
BIOCOMP
2006
13 years 10 months ago
Support Vector Machines for Predicting microRNA Hairpins
- microRNAs (miRNAs) are 20-22 nt noncoding RNAs which are rapidly emerging as crucial regulators of gene expression in plants and animals. Identification of the hairpins which yie...
Karol Szafranski, Molly Megraw, Martin Reczko, Art...
TNN
2008
97views more  TNN 2008»
13 years 9 months ago
Training Hard-Margin Support Vector Machines Using Greedy Stagewise Algorithm
Hard-margin support vector machines (HM-SVMs) suffer from getting overfitting in the presence of noise. Soft-margin SVMs deal with this problem by introducing a regularization term...
Liefeng Bo, Ling Wang, Licheng Jiao