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ICML
2009
IEEE
16 years 7 months ago
Multi-instance learning by treating instances as non-I.I.D. samples
Previous studies on multi-instance learning typically treated instances in the bags as independently and identically distributed. The instances in a bag, however, are rarely indep...
Zhi-Hua Zhou, Yu-Yin Sun, Yu-Feng Li
ICML
2007
IEEE
16 years 7 months ago
Large-scale RLSC learning without agony
The advances in kernel-based learning necessitate the study on solving a large-scale non-sparse positive definite linear system. To provide a deterministic approach, recent resear...
Wenye Li, Kin-Hong Lee, Kwong-Sak Leung
ICML
2006
IEEE
16 years 7 months ago
Learning low-rank kernel matrices
Kernel learning plays an important role in many machine learning tasks. However, algorithms for learning a kernel matrix often scale poorly, with running times that are cubic in t...
Brian Kulis, Inderjit S. Dhillon, Máty&aacu...
ICML
2006
IEEE
16 years 7 months ago
Learning user preferences for sets of objects
Most work on preference learning has focused on pairwise preferences or rankings over individual items. In this paper, we present a method for learning preferences over sets of it...
Marie desJardins, Eric Eaton, Kiri Wagstaff
ICML
2003
IEEE
16 years 7 months ago
An Analysis of Rule Evaluation Metrics
In this paper we analyze the most popular evaluation metrics for separate-and-conquer rule learning algorithms. Our results show that all commonly used heuristics, including accur...
Johannes Fürnkranz, Peter A. Flach