Titlebook: Discovery Science; 23rd International C Annalisa Appice,Grigorios Tsoumakas,Stan Matwin Conference proceedings 2020 Springer Nature Switzer

Antimicrobial

: Combining Active Learning and Weak Supervisionearning (ML). One well-known method to gain labeled data efficiently is Active Learning (AL), where the learner interactively asks human experts to label the most informative data point. Nevertheless, even by applying AL in labeling tasks the amount of human effort is still too high and should be mi

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Expiration

Dynamic Incremental Semi-supervised Fuzzy Clustering for Bipolar Disorder Episode Prediction mixed states. In this context, data collected through the interaction of patients with smartphones enable the creation of predictive models to support the early prediction of a starting episode. Previous research on predicting a new BD episode use mostly supervised learning methods that require lab

GRATE

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COVID-19 Therapy Target Discovery with Context-Aware Literature Mininge of automatically processing tens of thousands of scientific publications with the aim to enrich existing empirical evidence with literature-based associations is challenging and relevant. We propose a system for contextualization of empirical expression data by approximating relations between enti

Exposition

燕麦

Semantic Description of Data Mining Datasets: An Ontology-Based Annotation Schemacreasingly important. Consequently, nearly all community accepted guidelines and principles (e.g. FAIR and TRUST) for publishing such data in the digital ecosystem, stress the importance of semantic data enhancement. Having rich semantic annotation of DM datasets would support the data mining proces

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