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» On the Complexity of Function Learning
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BMCBI
2010
109views more  BMCBI 2010»
15 years 25 days ago
Predicting gene function using hierarchical multi-label decision tree ensembles
Background: S. cerevisiae, A. thaliana and M. musculus are well-studied organisms in biology and the sequencing of their genomes was completed many years ago. It is still a challe...
Leander Schietgat, Celine Vens, Jan Struyf, Hendri...
84
Voted
MFCS
2007
Springer
15 years 6 months ago
Evolvability
A framework for analyzing the computational capabilities and the limitations of the evolutionary process of random change guided by selection was recently introduced by Valiant [V...
Leslie G. Valiant
102
Voted
ICML
2010
IEEE
15 years 1 months ago
Bayesian Multi-Task Reinforcement Learning
We consider the problem of multi-task reinforcement learning where the learner is provided with a set of tasks, for which only a small number of samples can be generated for any g...
Alessandro Lazaric, Mohammad Ghavamzadeh
99
Voted
ICML
2006
IEEE
16 years 1 months ago
Iterative RELIEF for feature weighting
RELIEF is considered one of the most successful algorithms for assessing the quality of features. In this paper, we propose a set of new feature weighting algorithms that perform s...
Yijun Sun, Jian Li
98
Voted
WWW
2008
ACM
16 years 1 months ago
Dynamic cost-per-action mechanisms and applications to online advertising
We study the Cost-Per-Action or Cost-Per-Acquisition (CPA) charging scheme in online advertising. In this scheme, instead of paying per click, the advertisers pay only when a user...
Hamid Nazerzadeh, Amin Saberi, Rakesh Vohra