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» Learning from Highly Structured Data by Decomposition
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NIPS
2008
14 years 11 months ago
Predicting the Geometry of Metal Binding Sites from Protein Sequence
Metal binding is important for the structural and functional characterization of proteins. Previous prediction efforts have only focused on bonding state, i.e. deciding which prot...
Paolo Frasconi, Andrea Passerini
CATA
2003
14 years 11 months ago
Visualizing Query Structure
Visualization research seeks to exploit the human user’s ability to interpret graphical representations of data in order to provide insight into properties of the data that may ...
Dennis P. Groth
JMLR
2010
140views more  JMLR 2010»
14 years 4 months ago
Learning Non-Stationary Dynamic Bayesian Networks
Learning dynamic Bayesian network structures provides a principled mechanism for identifying conditional dependencies in time-series data. An important assumption of traditional D...
Joshua W. Robinson, Alexander J. Hartemink
NIPS
2007
14 years 11 months ago
Active Preference Learning with Discrete Choice Data
We propose an active learning algorithm that learns a continuous valuation model from discrete preferences. The algorithm automatically decides what items are best presented to an...
Eric Brochu, Nando de Freitas, Abhijeet Ghosh
BMCBI
2010
179views more  BMCBI 2010»
14 years 10 months ago
A semi-supervised learning approach to predict synthetic genetic interactions by combining functional and topological properties
Background: Genetic interaction profiles are highly informative and helpful for understanding the functional linkages between genes, and therefore have been extensively exploited ...
Zhuhong You, Zheng Yin, Kyungsook Han, De-Shuang H...