کد مقاله کد نشریه سال انتشار مقاله انگلیسی نسخه تمام متن
397805 1438508 2008 19 صفحه PDF دانلود رایگان
عنوان انگلیسی مقاله ISI
Adapting Bayes network structures to non-stationary domains
موضوعات مرتبط
مهندسی و علوم پایه مهندسی کامپیوتر هوش مصنوعی
پیش نمایش صفحه اول مقاله
Adapting Bayes network structures to non-stationary domains
چکیده انگلیسی

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.

ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: International Journal of Approximate Reasoning - Volume 49, Issue 2, October 2008, Pages 379-397