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» Learning to Rank Using an Ensemble of Lambda-Gradient Models
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CIKM
2009
Springer
14 years 13 days ago
Heterogeneous cross domain ranking in latent space
Traditional ranking mainly focuses on one type of data source, and effective modeling still relies on a sufficiently large number of labeled or supervised examples. However, in m...
Bo Wang, Jie Tang, Wei Fan, Songcan Chen, Zi Yang,...
ICML
2006
IEEE
14 years 8 months ago
Full Bayesian network classifiers
The structure of a Bayesian network (BN) encodes variable independence. Learning the structure of a BN, however, is typically of high computational complexity. In this paper, we e...
Jiang Su, Harry Zhang
RTCSA
2007
IEEE
14 years 2 months ago
Activity Recognition Based on Semi-supervised Learning
Activity recognition is a hot topic in context-aware computing. In activity recognition, machine learning techniques have been widely applied to learn the activity models from lab...
Donghai Guan, Weiwei Yuan, Young-Koo Lee, Andrey G...
CORR
2006
Springer
126views Education» more  CORR 2006»
13 years 7 months ago
Evaluating the Robustness of Learning from Implicit Feedback
This paper evaluates the robustness of learning from implicit feedback in web search. In particular, we create a model of user behavior by drawing upon user studies in laboratory ...
Filip Radlinski, Thorsten Joachims
ICDM
2010
IEEE
122views Data Mining» more  ICDM 2010»
13 years 5 months ago
Learning Preferences with Millions of Parameters by Enforcing Sparsity
We study the retrieval task that ranks a set of objects for a given query in the pairwise preference learning framework. Recently researchers found out that raw features (e.g. word...
Xi Chen, Bing Bai, Yanjun Qi, Qihang Lin, Jaime G....