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» On the Complexity of Function Learning
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RECOMB
2005
Springer
14 years 10 months ago
Predicting Protein-Peptide Binding Affinity by Learning Peptide-Peptide Distance Functions
Many important cellular response mechanisms are activated when a peptide binds to an appropriate receptor. In the immune system, the recognition of pathogen peptides begins when th...
Chen Yanover, Tomer Hertz
BMCBI
2006
111views more  BMCBI 2006»
13 years 10 months ago
PepDist: A New Framework for Protein-Peptide Binding Prediction based on Learning Peptide Distance Functions
Background: Many different aspects of cellular signalling, trafficking and targeting mechanisms are mediated by interactions between proteins and peptides. Representative examples...
Tomer Hertz, Chen Yanover
JMLR
2010
147views more  JMLR 2010»
13 years 4 months ago
Gaussian Processes for Machine Learning (GPML) Toolbox
The GPML toolbox provides a wide range of functionality for Gaussian process (GP) inference and prediction. GPs are specified by mean and covariance functions; we offer a library ...
Carl Edward Rasmussen, Hannes Nickisch
COLT
2005
Springer
14 years 3 months ago
Learnability of Bipartite Ranking Functions
The problem of ranking, in which the goal is to learn a real-valued ranking function that induces a ranking or ordering over an instance space, has recently gained attention in mac...
Shivani Agarwal, Dan Roth
PKDD
2009
Springer
181views Data Mining» more  PKDD 2009»
14 years 4 months ago
Active Learning for Reward Estimation in Inverse Reinforcement Learning
Abstract. Inverse reinforcement learning addresses the general problem of recovering a reward function from samples of a policy provided by an expert/demonstrator. In this paper, w...
Manuel Lopes, Francisco S. Melo, Luis Montesano