18 search results - page 2 / 4 » Toward Optimal Active Learning through Sampling Estimation o... |
NIPS
2001
13 years 6 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...
PKDD
2009
Springer
13 years 9 months ago
2009
Springer
Abstract. This paper focuses on Active Learning with a limited number of queries; in application domains such as Numerical Engineering, the size of the training set might be limite...
NN
2002
Springer
2002
Springer
Optimal design of regularization term and regularization parameter by subspace information criterion
13 years 4 months ago
The problem of designing the regularization term and regularization parameter for linear regression models is discussed. Previously, we derived an approximation to the generalizat...
AAAI
2008
13 years 7 months ago
2008
Off-policy reinforcement learning is aimed at efficiently reusing data samples gathered in the past, which is an essential problem for physically grounded AI as experiments are us...
ICCV
2009
IEEE
14 years 9 months ago
2009
IEEE
We present a manifold learning approach to dimensionality
reduction that explicitly models the manifold as a mapping
from low to high dimensional space. The manifold is
represen...