200 search results - page 10 / 40 » On the Consistency of Discrete Bayesian Learning |
ESOP
2011
Springer
12 years 11 months ago
2011
Springer
Abstract. The Bayesian approach to machine learning amounts to inferring posterior distributions of random variables from a probabilistic model of how the variables are related (th...
PKDD
2010
Springer
13 years 6 months ago
2010
Springer
Abstract. Bayesian reinforcement learning (RL) is aimed at making more efficient use of data samples, but typically uses significantly more computation. For discrete Markov Decis...
DIS
2006
Springer
13 years 11 months ago
2006
Springer
In supervised machine learning, variable ranking aims at sorting the input variables according to their relevance w.r.t. an output variable. In this paper, we propose a new relevan...
ICRA
2007
IEEE
14 years 2 months ago
2007
IEEE
— Recent advances in machine learning and adaptive motor control have enabled efficient techniques for online learning of stationary plant dynamics and it’s use for robust pre...
NIPS
2004
13 years 9 months ago
2004
We develop a family of upper and lower bounds on the worst-case expected KL loss for estimating a discrete distribution on a finite number m of points, given N i.i.d. samples. Our...