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» Lazy Learning for Improving Ranking of Decision Trees
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KDD
2010
ACM
257views Data Mining» more  KDD 2010»
13 years 11 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...
CIKM
2009
Springer
14 years 2 months ago
Learning to rank from Bayesian decision inference
Ranking is a key problem in many information retrieval (IR) applications, such as document retrieval and collaborative filtering. In this paper, we address the issue of learning ...
Jen-Wei Kuo, Pu-Jen Cheng, Hsin-Min Wang
ECML
2005
Springer
14 years 29 days ago
Simple Test Strategies for Cost-Sensitive Decision Trees
We study cost-sensitive learning of decision trees that incorporate both test costs and misclassification costs. In particular, we first propose a lazy decision tree learning that ...
Shengli Sheng, Charles X. Ling, Qiang Yang
ISSRE
2007
IEEE
13 years 9 months ago
Using Machine Learning to Support Debugging with Tarantula
Using a specific machine learning technique, this paper proposes a way to identify suspicious statements during debugging. The technique is based on principles similar to Tarantul...
Lionel C. Briand, Yvan Labiche, Xuetao Liu
AAAI
2006
13 years 8 months ago
Anytime Induction of Decision Trees: An Iterative Improvement Approach
Most existing decision tree inducers are very fast due to their greedy approach. In many real-life applications, however, we are willing to allocate more time to get better decisi...
Saher Esmeir, Shaul Markovitch