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ILP
2001
Springer
14 years 5 days ago
Learning Functions from Imperfect Positive Data
The Bayesian framework of learning from positive noise-free examples derived by Muggleton [12] is extended to learning functional hypotheses from positive examples containing norma...
Filip Zelezný
BMCBI
2007
128views more  BMCBI 2007»
13 years 7 months ago
A robust and efficient algorithm for the shape description of protein structures and its application in predicting ligand bindin
Background: An accurate description of protein shape derived from protein structure is necessary to establish an understanding of protein-ligand interactions, which in turn will l...
Lei Xie, Philip E. Bourne
ICCV
2009
IEEE
13 years 5 months ago
Local distance functions: A taxonomy, new algorithms, and an evaluation
We present a taxonomy for local distance functions where most existing algorithms can be regarded as approximations of the geodesic distance defined by a metric tensor. We categor...
Deva Ramanan, Simon Baker
ILP
2004
Springer
14 years 1 months ago
Learning an Approximation to Inductive Logic Programming Clause Evaluation
One challenge faced by many Inductive Logic Programming (ILP) systems is poor scalability to problems with large search spaces and many examples. Randomized search methods such as ...
Frank DiMaio, Jude W. Shavlik
ICML
2008
IEEE
14 years 8 months ago
Efficiently learning linear-linear exponential family predictive representations of state
Exponential Family PSR (EFPSR) models capture stochastic dynamical systems by representing state as the parameters of an exponential family distribution over a shortterm window of...
David Wingate, Satinder P. Singh