142 search results - page 2 / 29 » Learning Graphical Model Structure Using L1-Regularization P... |
ATAL
2008
Springer
13 years 9 months ago
2008
Springer
The goal of Reinforcement learning (RL) is to maximize reward (minimize cost) in a Markov decision process (MDP) without knowing the underlying model a priori. RL algorithms tend ...
UAI
2008
13 years 8 months ago
2008
We consider the task of learning mappings from sequential data to real-valued responses. We present and evaluate an approach to learning a type of hidden Markov model (HMM) for re...
ICML
2009
IEEE
14 years 8 months ago
2009
IEEE
Recent work has shown that one can learn the structure of Gaussian Graphical Models by imposing an L1 penalty on the precision matrix, and then using efficient convex optimization...
ICCV
2009
IEEE
15 years 10 days ago
2009
IEEE
We present a novel approach for detecting global behaviour
anomalies in multiple disjoint cameras by learning
time delayed dependencies between activities cross camera
views. Sp...
CORR
2012
Springer
12 years 3 months ago
2012
Springer
We show that the log-likelihood of several probabilistic graphical models is Lipschitz continuous with respect to the p-norm of the parameters. We discuss several implications ...