481 search results - page 12 / 97 » Learning relational probability trees |
ML
2011
ACM
13 years 2 months ago
2011
ACM
Abstract. Type Extension Trees (TET) have been recently introduced as an expressive representation language allowing to encode complex combinatorial features of relational entities...
NIPS
2001
13 years 9 months ago
2001
We describe the g-factor which relates probability distributions on image features to distributions on the images themselves. The g-factor depends only on our choice of features a...
VLDB
2006
ACM
14 years 8 months ago
2006
ACM
We focus on the problem of efficient learning of dependency trees. Once grown, they can be used as a special case of a Bayesian network, for PDF approximation, and for many other u...
ADMA
2006
Springer
13 years 11 months ago
2006
Springer
Abstract. Most of the work in Machine Learning assume that examples are generated at random according to some stationary probability distribution. In this work we study the problem...
ICML
2005
IEEE
14 years 8 months ago
2005
IEEE
We apply nonparametric hierarchical Bayesian modelling to relational learning. In a hierarchical Bayesian approach, model parameters can be "personalized", i.e., owned b...