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ESANN
2008
15 years 8 months ago
Learning Inverse Dynamics: a Comparison
While it is well-known that model can enhance the control performance in terms of precision or energy efficiency, the practical application has often been limited by the complexiti...
Duy Nguyen-Tuong, Jan Peters, Matthias Seeger, Ber...
ECML
2006
Springer
15 years 10 months ago
Margin-Based Active Learning for Structured Output Spaces
In many complex machine learning applications there is a need to learn multiple interdependent output variables, where knowledge of these interdependencies can be exploited to impr...
Dan Roth, Kevin Small
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
HICSS
2006
IEEE
163views Biometrics» more  HICSS 2006»
16 years 23 days ago
Learning Ranking vs. Modeling Relevance
The classical (ad hoc) document retrieval problem has been traditionally approached through ranking according to heuristically developed functions (such as tf.idf or bm25) or gene...
Dmitri Roussinov, Weiguo Fan
SDM
2010
SIAM
144views Data Mining» more  SDM 2010»
15 years 8 months ago
A Probabilistic Framework to Learn from Multiple Annotators with Time-Varying Accuracy
This paper addresses the challenging problem of learning from multiple annotators whose labeling accuracy (reliability) differs and varies over time. We propose a framework based ...
Pinar Donmez, Jaime G. Carbonell, Jeff Schneider