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» Learning the Common Structure of Data
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ISMB
1993
15 years 7 months ago
Protein Structure Prediction: Selecting Salient Features from Large Candidate Pools
Weintroduce a parallel approach, "DT-SELECT," for selecting features used by inductive learning algorithms to predict protein secondary structure. DT-SELECTis able to ra...
Kevin J. Cherkauer, Jude W. Shavlik
EMNLP
2009
15 years 4 months ago
An Empirical Study of Semi-supervised Structured Conditional Models for Dependency Parsing
This paper describes an empirical study of high-performance dependency parsers based on a semi-supervised learning approach. We describe an extension of semisupervised structured ...
Jun Suzuki, Hideki Isozaki, Xavier Carreras, Micha...
DILS
2005
Springer
15 years 8 months ago
Assigning Unique Keys to Chemical Compounds for Data Integration: Some Interesting Counter Examples
Integrating data involving chemical structures is simplified when unique identifiers (UIDs) can be associated with chemical structures. For example, these identifiers can be use...
Greeshma Neglur, Robert L. Grossman, Bing Liu
UAI
1996
15 years 7 months ago
Bayesian Learning of Loglinear Models for Neural Connectivity
This paper presents a Bayesian approach to learning the connectivity structure of a group of neurons from data on configuration frequencies. A major objective of the research is t...
Kathryn B. Laskey, Laura Martignon
ECML
2001
Springer
15 years 10 months ago
Discovering Admissible Simultaneous Equation Models from Observed Data
Conventional work on scienti c discovery such as BACON derives empirical law equations from experimental data. In recent years, SDS introducing mathematical admissibility constrain...
Takashi Washio, Hiroshi Motoda, Yuji Niwa