44 search results - page 3 / 9 » Bayesian learning for a class of priors with prescribed marg... |
ML
2002
ACM
13 years 5 months ago
2002
ACM
I describe a framework for interpreting Support Vector Machines (SVMs) as maximum a posteriori (MAP) solutions to inference problems with Gaussian Process priors. This probabilisti...
AAAI
2010
13 years 6 months ago
2010
Bayesian inference is an appealing approach for leveraging prior knowledge in reinforcement learning (RL). In this paper we describe an algorithm for discovering different classes...
ESOP
2011
Springer
12 years 8 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...
ICCV
2009
IEEE
13 years 3 months ago
2009
IEEE
Poisson regression models the noisy output of a counting function as a Poisson random variable, with a log-mean parameter that is a linear function of the input vector. In this wo...
JMLR
2012
11 years 7 months ago
2012
To relax the homogeneity assumption of classical dynamic Bayesian networks (DBNs), various recent studies have combined DBNs with multiple changepoint processes. The underlying as...