Interpretability of learned models is considered from the point of view of a system using the model as a component, for tasks such as reasoning about its ...
Bayesian network classifiers can be represented as polynomial threshold functions (PTF). ... Interpretability of Bayesian network classifiers: OBDD approximation.
Jan 17, 2020 · Turán, Interpretability of Bayesian Network Classifiers: OBDD. Approximation and Polynomial Threshold Functions, 2019, Submitted. 3. Master's ...
Interpretability of Bayesian Network Classifiers: OBDD Approximation and Polynomial Threshold Functions. ... Bayesian network classifiers with OBDD. PGM ...
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This paper presents an algorithm for converting any naive Bayes classifier into an ODD, and it is shown theoretically and experimentally that this algorithm ...
Jul 21, 2023 · Interpretability of bayesian network classifiers: Obdd approximation and polynomial threshold functions. In International Symposium on.
Jul 3, 2020 · Interpretability of bayesian network classifiers: Obdd approximation and poly- nomial threshold functions. In International Symposium on.
It is shown that Bayesian network classifiers of tree-width k have an OBDD approximation computable in polynomial time in the parameters, for every fixed k.
Missing: Interpretability Functions.
Jul 17, 2019 · An algorithm is proposed for compiling Bayesian network classifiers into decision graphs that mimic the input and output behavior of the ...
Interpretability of bayesian network classifiers: Obdd approximation and poly- nomial threshold functions. ... and other linear classifiers with polynomial ...