5 search results - page 1 / 1 » Finite-Sample Convergence Rates for Q-Learning and Indirect ... |
NIPS
1998
13 years 6 months ago
1998
In this paper, we address two issues of long-standing interest in the reinforcement learning literature. First, what kinds of performance guarantees can be made for Q-learning aft...
NIPS
2007
13 years 6 months ago
2007
Over the past few years, the notion of stability in data clustering has received growing attention as a cluster validation criterion in a sample-based framework. However, recent w...
TIT
2002
13 years 4 months ago
2002
Abstract--The Lagrangian formulation of variable-rate vector quantization is known to yield useful necessary conditions for quantizer optimality and generalized Lloyd algorithms fo...
NIPS
2008
13 years 6 months ago
2008
Empirical evidence shows that in favorable situations semi-supervised learning (SSL) algorithms can capitalize on the abundance of unlabeled training data to improve the performan...
CVPR
2008
IEEE
14 years 6 months ago
2008
IEEE
Gaussian blurring mean-shift (GBMS) is a nonparametric clustering algorithm, having a single bandwidth parameter that controls the number of clusters. The algorithm iteratively sh...