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NIPS
2007
15 years 3 months ago
Direct Importance Estimation with Model Selection and Its Application to Covariate Shift Adaptation
A situation where training and test samples follow different input distributions is called covariate shift. Under covariate shift, standard learning methods such as maximum likeli...
Masashi Sugiyama, Shinichi Nakajima, Hisashi Kashi...
123
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RSS
2007
159views Robotics» more  RSS 2007»
15 years 3 months ago
Gaussian Beam Processes: A Nonparametric Bayesian Measurement Model for Range Finders
— In probabilistic mobile robotics, the development of measurement models plays a crucial role as it directly influences the efficiency and the robustness of the robot’s perf...
Christian Plagemann, Kristian Kersting, Patrick Pf...
AAAI
2004
15 years 3 months ago
Self-Organizing Visual Maps
This paper deals with automatically learning the spatial distribution of a set of images. That is, given a sequence of images acquired from well-separated locations, how can they ...
Robert Sim, Gregory Dudek
AAAI
2006
15 years 3 months ago
Sound and Efficient Inference with Probabilistic and Deterministic Dependencies
Reasoning with both probabilistic and deterministic dependencies is important for many real-world problems, and in particular for the emerging field of statistical relational lear...
Hoifung Poon, Pedro Domingos
114
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ACL
2006
15 years 3 months ago
Modelling Lexical Redundancy for Machine Translation
Certain distinctions made in the lexicon of one language may be redundant when translating into another language. We quantify redundancy among source types by the similarity of th...
David Talbot, Miles Osborne