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SIGIR
2008
ACM
15 years 6 months ago
Personalized active learning for collaborative filtering
Collaborative Filtering (CF) requires user-rated training examples for statistical inference about the preferences of new users. Active learning strategies identify the most infor...
Abhay Harpale, Yiming Yang
ML
1998
ACM
15 years 6 months ago
Conjectural Equilibrium in Multiagent Learning
Abstract. Learning in a multiagent environment is complicated by the fact that as other agents learn, the environment effectively changes. Moreover, other agents’ actions are oft...
Michael P. Wellman, Junling Hu
ICDM
2010
IEEE
122views Data Mining» more  ICDM 2010»
15 years 4 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....
PAMI
2011
15 years 1 months ago
Semi-Supervised Learning via Regularized Boosting Working on Multiple Semi-Supervised Assumptions
—Semi-supervised learning concerns the problem of learning in the presence of labeled and unlabeled data. Several boosting algorithms have been extended to semi-supervised learni...
Ke Chen, Shihai Wang
HICSS
2009
IEEE
147views Biometrics» more  HICSS 2009»
16 years 1 months ago
Determining a Firm's Optimal Outsourcing Rate: A Learning Model Perspective
We present a decision model of a firm’s optimal outsourcing rate as an extension of Cha et. al [1]’s previous work on the economic risk of knowledge loss and deskilling in the...
Hoon S. Cha, David E. Pingry, Matt E. Thatcher