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» Optimal feature selection for support vector machines
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ICML
2009
IEEE
14 years 8 months ago
Regularization and feature selection in least-squares temporal difference learning
We consider the task of reinforcement learning with linear value function approximation. Temporal difference algorithms, and in particular the Least-Squares Temporal Difference (L...
J. Zico Kolter, Andrew Y. Ng
CIBCB
2005
IEEE
13 years 9 months ago
A Transcriptional Approach to Gene Clustering
— We present an integrative method for clustering coregulated genes and elucidating their underlying regulatory mechanisms. We use multi-state partition functions and thermodynam...
Ilias Tagkopoulos
SIGKDD
2008
150views more  SIGKDD 2008»
13 years 7 months ago
Learning to improve area-under-FROC for imbalanced medical data classification using an ensemble method
This paper presents our solution for KDD Cup 2008 competition that aims at optimizing the area under ROC for breast cancer detection. We exploited weighted-based classification me...
Hung-Yi Lo, Chun-Min Chang, Tsung-Hsien Chiang, Ch...
KDD
2012
ACM
205views Data Mining» more  KDD 2012»
11 years 10 months ago
Rank-loss support instance machines for MIML instance annotation
Multi-instance multi-label learning (MIML) is a framework for supervised classification where the objects to be classified are bags of instances associated with multiple labels....
Forrest Briggs, Xiaoli Z. Fern, Raviv Raich
ICML
2009
IEEE
14 years 2 months ago
Fast evolutionary maximum margin clustering
The maximum margin clustering approach is a recently proposed extension of the concept of support vector machines to the clustering problem. Briefly stated, it aims at finding a...
Fabian Gieseke, Tapio Pahikkala, Oliver Kramer