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EMNLP
2008
15 years 7 months ago
Discriminative Learning of Selectional Preference from Unlabeled Text
We present a discriminative method for learning selectional preferences from unlabeled text. Positive examples are taken from observed predicate-argument pairs, while negatives ar...
Shane Bergsma, Dekang Lin, Randy Goebel
NIPS
1998
15 years 7 months ago
Learning from Dyadic Data
Dyadic data refers to a domain with two nite sets of objects in which observations are made for dyads, i.e., pairs with one element from either set. This type of data arises natur...
Thomas Hofmann, Jan Puzicha, Michael I. Jordan
CVPR
2009
IEEE
17 years 1 months ago
Learning from Ambiguously Labeled Images
In many image and video collections, we have access only to partially labeled data. For example, personal photo collections often contain several faces per image and a caption t...
Benjamin Sapp, Benjamin Taskar, Chris Jordan, Timo...
ICIP
2008
IEEE
16 years 8 months ago
Learning action dictionaries from video
Summarizing the contents of a video containing human activities is an important problem in computer vision and has important applications in automated surveillance systems. Summar...
Pavan K. Turaga, Rama Chellappa
ALT
2006
Springer
16 years 3 months ago
Iterative Learning from Positive Data and Negative Counterexamples
A model for learning in the limit is defined where a (so-called iterative) learner gets all positive examples from the target language, tests every new conjecture with a teacher ...
Sanjay Jain, Efim B. Kinber