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Optimizing Probabilistic Models for Relational Sequence Learning
[chapter]
2011
Lecture Notes in Computer Science
This paper tackles the problem of relational sequence learning selecting relevant features elicited from a set of labelled sequences. Each relational sequence is firstly mapped into a feature vector using the result of a feature construction method. The second step finds an optimal subset of the constructed features that leads to high classification accuracy, by adopting a wrapper approach that uses a stochastic local search algorithm embedding a Bayes classifier. The performance of the
doi:10.1007/978-3-642-21916-0_27
fatcat:ooonnvoixbekflc5geb5lg6tri