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KDD
2010
ACM
257views Data Mining» more  KDD 2010»
13 years 10 months ago
Multi-task learning for boosting with application to web search ranking
In this paper we propose a novel algorithm for multi-task learning with boosted decision trees. We learn several different learning tasks with a joint model, explicitly addressing...
Olivier Chapelle, Pannagadatta K. Shivaswamy, Srin...
PKDD
2009
Springer
118views Data Mining» more  PKDD 2009»
14 years 26 days ago
The Feature Importance Ranking Measure
Most accurate predictions are typically obtained by learning machines with complex feature spaces (as e.g. induced by kernels). Unfortunately, such decision rules are hardly access...
Alexander Zien, Nicole Krämer, Sören Son...
ROCAI
2004
Springer
13 years 11 months ago
Learning Interestingness Measures in Terminology Extraction. A ROC-based approach
Abstract. In the field of Text Mining, a key phase in data preparation is concerned with the extraction of terms, i.e. collocation of words attached to specific concepts (e.g. Ph...
Mathieu Roche, Jérôme Azé, Yve...
SIGIR
2008
ACM
13 years 6 months ago
Learning to rank with partially-labeled data
Ranking algorithms, whose goal is to appropriately order a set of objects/documents, are an important component of information retrieval systems. Previous work on ranking algorith...
Kevin Duh, Katrin Kirchhoff
CORR
2008
Springer
116views Education» more  CORR 2008»
13 years 6 months ago
Learning to rank with combinatorial Hodge theory
Abstract. We propose a number of techniques for learning a global ranking from data that may be incomplete and imbalanced -- characteristics that are almost universal to modern dat...
Xiaoye Jiang, Lek-Heng Lim, Yuan Yao, Yinyu Ye