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» MILIS: Multiple Instance Learning with Instance Selection
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NIPS
2007
13 years 9 months ago
Learning and using relational theories
Much of human knowledge is organized into sophisticated systems that are often called intuitive theories. We propose that intuitive theories are mentally represented in a logical ...
Charles Kemp, Noah Goodman, Joshua B. Tenenbaum
ECML
2007
Springer
14 years 1 months ago
Optimizing Feature Sets for Structured Data
Choosing a suitable feature representation for structured data is a non-trivial task due to the vast number of potential candidates. Ideally, one would like to pick a small, but in...
Ulrich Rückert, Stefan Kramer
COLING
2010
13 years 2 months ago
Boosting Relation Extraction with Limited Closed-World Knowledge
This paper presents a new approach to improving relation extraction based on minimally supervised learning. By adding some limited closed-world knowledge for confidence estimation...
Feiyu Xu, Hans Uszkoreit, Sebastian Krause, Hong L...
APWEB
2009
Springer
13 years 11 months ago
Ontology Evaluation through Text Classification
We present a new method to evaluate a search ontology, which relies on mapping ontology instances to textual documents. On the basis of this mapping, we evaluate the adequacy of on...
Yael Dahan Netzer, David Gabay, Meni Adler, Yoav G...
IDA
2007
Springer
13 years 7 months ago
Removing biases in unsupervised learning of sequential patterns
Unsupervised sequence learning is important to many applications. A learner is presented with unlabeled sequential data, and must discover sequential patterns that characterize th...
Yoav Horman, Gal A. Kaminka