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» Feature selection for ranking using boosted trees
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ICMLA
2008
15 years 1 months ago
Predicting Algorithm Accuracy with a Small Set of Effective Meta-Features
We revisit 26 meta-features typically used in the context of meta-learning for model selection. Using visual analysis and computational complexity considerations, we find 4 meta-f...
Jun Won Lee, Christophe G. Giraud-Carrier
ML
2012
ACM
413views Machine Learning» more  ML 2012»
13 years 7 months ago
Gradient-based boosting for statistical relational learning: The relational dependency network case
Dependency networks approximate a joint probability distribution over multiple random variables as a product of conditional distributions. Relational Dependency Networks (RDNs) are...
Sriraam Natarajan, Tushar Khot, Kristian Kersting,...
133
Voted
CIVR
2008
Springer
279views Image Analysis» more  CIVR 2008»
15 years 1 months ago
Semi-supervised learning of object categories from paired local features
This paper presents a semi-supervised learning (SSL) approach to find similarities of images using statistics of local matches. SSL algorithms are well known for leveraging a larg...
Wen Wu, Jie Yang
KDID
2003
119views Database» more  KDID 2003»
15 years 1 months ago
Generalized Version Space Trees
We introduce generalized version space trees, a novel data structure that serves as a condensed representation in inductive databases for graph mining. Generalized version space tr...
Ulrich Rückert, Stefan Kramer
CIBCB
2006
IEEE
15 years 1 months ago
A New Hybrid Approach for Unsupervised Gene Selection
In recent years, unsupervised gene (feature) selection has become an integral part of microarray analysis because of the large number of genes and complexity in biological systems....
Young Bun Kim, Jean Gao