509 search results - page 14 / 102 » Using Learning for Approximation in Stochastic Processes |
JMLR
2010
13 years 5 months ago
2010
Sparse coding—that is, modelling data vectors as sparse linear combinations of basis elements—is widely used in machine learning, neuroscience, signal processing, and statisti...
ILP
2004
Springer
14 years 23 days ago
2004
Springer
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 ...
ICML
2010
IEEE
13 years 8 months ago
2010
IEEE
Many structured prediction tasks involve complex models where inference is computationally intractable, but where it can be well approximated using a linear programming relaxation...
NIPS
2008
13 years 8 months ago
2008
Stochastic relational models (SRMs) [15] provide a rich family of choices for learning and predicting dyadic data between two sets of entities. The models generalize matrix factor...
NIPS
2001
13 years 8 months ago
2001
Policy gradient methods for reinforcement learning avoid some of the undesirable properties of the value function approaches, such as policy degradation (Baxter and Bartlett, 2001...