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NAACL
2007
14 years 12 months ago
A Cascaded Machine Learning Approach to Interpreting Temporal Expressions
A new architecture for identifying and interpreting temporal expressions is introduced, in which the large set of complex hand-crafted rules standard in systems for this task is r...
David Ahn, Joris van Rantwijk, Maarten de Rijke
ICML
2008
IEEE
15 years 11 months ago
Learning to classify with missing and corrupted features
After a classifier is trained using a machine learning algorithm and put to use in a real world system, it often faces noise which did not appear in the training data. Particularl...
Ofer Dekel, Ohad Shamir
ECML
2007
Springer
15 years 4 months ago
Learning to Classify Documents with Only a Small Positive Training Set
Many real-world classification applications fall into the class of positive and unlabeled (PU) learning problems. In many such applications, not only could the negative training ex...
Xiaoli Li, Bing Liu, See-Kiong Ng
CVPR
2011
IEEE
14 years 6 months ago
Distributed Computer Vision Algorithms Through Distributed Averaging
Traditional computer vision and machine learning algorithms have been largely studied in a centralized setting, where all the processing is performed at a single central location....
Roberto Tron, René, Vidal

Book
778views
16 years 8 months ago
Gaussian Processes for Machine Learning
"Gaussian processes (GPs) provide a principled, practical, probabilistic approach to learning in kernel machines. GPs have received increased attention in the machine-learning...
Carl Edward Rasmussen and Christopher K. I. Willia...