Adapting Bayes network structures to non-stationary domains
نویسندگان
چکیده
منابع مشابه
Adapting Bayes Network Structures to Non-stationary Domains
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 incremen...
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When an incremental structural learning method gradually modifies a Bayesian network (BN) structure to fit observations, as they are read from a database, 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 to be gradually constructed as observations of the environment are made. Existing algorithms f...
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Ever since Pearl (1988) published his seminal book on Bayesian networks (BNs), the formalism has become a widespread tool for representing, eliciting, and discovering probabilistic relationships for problem domains defined over discrete variables. One area of research that has seen much activity is the area of learning the structure of BNs, where probabilistic relationships for variables are di...
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The goal of a learner in standard online learning is to have the cumulative loss not much larger compared with the best-performing prediction-function from some fixed class. Numerous algorithms were shown to have this gap arbitrarily close to zero compared with the best function that is chosen off-line. Nevertheless, many real-world applications (such as adaptive filtering) are nonstationary in...
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ژورنال
عنوان ژورنال: International Journal of Approximate Reasoning
سال: 2008
ISSN: 0888-613X
DOI: 10.1016/j.ijar.2008.02.007