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» A Framework for Multiple-Instance Learning
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ICML
2003
IEEE
16 years 5 months ago
Learning on the Test Data: Leveraging Unseen Features
This paper addresses the problem of classification in situations where the data distribution is not homogeneous: Data instances might come from different locations or times, and t...
Benjamin Taskar, Ming Fai Wong, Daphne Koller
KDD
2008
ACM
147views Data Mining» more  KDD 2008»
16 years 5 months ago
Structured learning for non-smooth ranking losses
Learning to rank from relevance judgment is an active research area. Itemwise score regression, pairwise preference satisfaction, and listwise structured learning are the major te...
Soumen Chakrabarti, Rajiv Khanna, Uma Sawant, Chir...
ATAL
2007
Springer
15 years 11 months ago
Advice taking in multiagent reinforcement learning
This paper proposes the β-WoLF algorithm for multiagent reinforcement learning (MARL) in the stochastic games framework that uses an additional “advice” signal to inform agen...
Michael Rovatsos, Alexandros Belesiotis
MLG
2007
Springer
15 years 11 months ago
A Universal Kernel for Learning Regular Languages
We give a universal kernel that renders all the regular languages linearly separable. We are not able to compute this kernel efficiently and conjecture that it is intractable, but...
Leonid Kontorovich
125
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FOCS
2005
IEEE
15 years 10 months ago
Learning mixtures of product distributions over discrete domains
We consider the problem of learning mixtures of product distributions over discrete domains in the distribution learning framework introduced by Kearns et al. [18]. We give a poly...
Jon Feldman, Ryan O'Donnell, Rocco A. Servedio