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Publication
Discriminative Bias for Learning Probabilistic Sentential Decision Diagrams
Book Contribution - Book Chapter Conference Contribution
Methods that learn the structure of Probabilistic Sentential Decision Diagrams (PSDD) from data have achieved state-of-the-art performance in tractable learning tasks. These methods learn PSDDs incrementally by optimizing the likelihood of the induced probability distribution given available data and are thus robust against missing values, a relevant trait to address the challenges of embedded applications, such as failing sensors and resource constraints. However PSDDs are outperformed by discriminatively trained models in classification tasks. In this work, we introduce D-LearnPSDD, a learner that improves the classification performance of the LearnPSDD algorithm by introducing a discriminative bias that encodes the conditional relation between the class and feature variables.
Book: Advances in Intelligent Data Analysis XVIII
Pages: 184 - 196
ISBN:978-3-030-44584-3
Publication year:2020
BOF-keylabel:yes
IOF-keylabel:yes
Authors from:Higher Education
Accessibility:Open