Abstract
When an incremental structural learning method gradually
modifies a Bayesian network (BN) structure to fit a sequential stream
of observations, we call the process structural
adaptation. Structural adaptation is useful when the learner is set to
work in an unknown environment, where a BN is gradually
being constructed as observations of the environment are made. Existing
algorithms for incremental learning assume that the samples in the
database have been drawn from a single underlying distribution. In
this paper we relax this assumption, so that the underlying
distribution can change during the sampling of the database. The
proposed method can thus be used in unknown environments, where
it is not even known whether the dynamics of the environment are
stable. We state formal correctness results for our method,
and demonstrate its feasibility experimentally
modifies a Bayesian network (BN) structure to fit a sequential stream
of observations, we call the process structural
adaptation. Structural adaptation is useful when the learner is set to
work in an unknown environment, where a BN is gradually
being constructed as observations of the environment are made. Existing
algorithms for incremental learning assume that the samples in the
database have been drawn from a single underlying distribution. In
this paper we relax this assumption, so that the underlying
distribution can change during the sampling of the database. The
proposed method can thus be used in unknown environments, where
it is not even known whether the dynamics of the environment are
stable. We state formal correctness results for our method,
and demonstrate its feasibility experimentally
Original language | English |
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Journal | International Journal of Approximate Reasoning |
Volume | 49 |
Issue number | 2 |
Pages (from-to) | 379-397 |
ISSN | 0888-613X |
DOIs | |
Publication status | Published - 2008 |
Keywords
- Bayesian networks
- Learning
- Adaption
- Non-stationary domains