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» A graphical model for protein secondary structure prediction
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NIPS
2008
13 years 10 months ago
Structured ranking learning using cumulative distribution networks
Ranking is at the heart of many information retrieval applications. Unlike standard regression or classification in which we predict outputs independently, in ranking we are inter...
Jim C. Huang, Brendan J. Frey
BMCBI
2004
111views more  BMCBI 2004»
13 years 9 months ago
The Hotdog fold: wrapping up a superfamily of thioesterases and dehydratases
Background: The Hotdog fold was initially identified in the structure of Escherichia coli FabA and subsequently in 4-hydroxybenzoyl-CoA thioesterase from Pseudomonas sp. strain CB...
Shane C. Dillon, Alex Bateman
ICANN
2007
Springer
14 years 3 months ago
Structure Learning with Nonparametric Decomposable Models
Abstract. We present a novel approach to structure learning for graphical models. By using nonparametric estimates to model clique densities in decomposable models, both discrete a...
Anton Schwaighofer, Mathäus Dejori, Volker Tr...
AAAI
2008
13 years 11 months ago
Structure Learning on Large Scale Common Sense Statistical Models of Human State
Research has shown promise in the design of large scale common sense probabilistic models to infer human state from environmental sensor data. These models have made use of mined ...
William Pentney, Matthai Philipose, Jeff A. Bilmes
CIKM
2008
Springer
13 years 11 months ago
Intra-document structural frequency features for semi-supervised domain adaptation
In this work we try to bridge the gap often encountered by researchers who find themselves with few or no labeled examples from their desired target domain, yet still have access ...
Andrew Arnold, William W. Cohen