136 search results - page 21 / 28 » On Learning Monotone Boolean Functions |
ICANN
2007
Springer
14 years 1 months ago
2007
Springer
Finding the largest linearly separable set of examples for a given Boolean function is a NP-hard problem, that is relevant to neural network learning algorithms and to several prob...
PAMI
2006
13 years 7 months ago
2006
Gaussian process classifiers (GPCs) are Bayesian probabilistic kernel classifiers. In GPCs, the probability of belonging to a certain class at an input location is monotonically re...
DAGM
2004
Springer
14 years 1 months ago
2004
Springer
We present an approach to discretizing multivariate continuous data while learning the structure of a graphical model. We derive the joint scoring function from the principle of p...
ICML
2004
IEEE
14 years 8 months ago
2004
IEEE
This paper extends previous work on the Skewing algorithm, a promising approach that allows greedy decision tree induction algorithms to handle problematic functions such as parit...
ECML
2006
Springer
13 years 11 months ago
2006
Springer
Causal independence modelling is a well-known method both for reducing the size of probability tables and for explaining the underlying mechanisms in Bayesian networks. In this pap...