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Recognizing input space and target concept drifts in data streams with scarcely labeled and unlabelled instances.

, , , , and . Inf. Sci., (2016)

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MapView: Graphical Data Representation for Active Learning., , , , , , , and . AL@iKNOW, volume 1707 of CEUR Workshop Proceedings, page 3-8. CEUR-WS.org, (2016)Multi-objective knowledge-based strategy for process parameter optimization in Micro-fluidic chip production., , , , , and . SSCI, page 1-8. IEEE, (2017)Advanced linguistic explanations of classifier decisions for users' annotation support., , , , , and . IEEE Conf. on Intelligent Systems, page 421-432. IEEE, (2016)Dynamic Inclusion of New Event Types in Visual Inspection Using Evolving Classifiers., , , and . ICMLA, page 487-494. IEEE, (2014)A soft-computing framework for automated optimization of multiple product quality criteria with application to micro-fluidic chip production., , , , and . Appl. Soft Comput., (2021)On improving performance of surface inspection systems by online active learning and flexible classifier updates., , , , and . Mach. Vis. Appl., 27 (1): 103-127 (2016)On-line anomaly detection with advanced independent component analysis of multi-variate residual signals from causal relation networks., , , , , , , and . Inf. Sci., (2020)Integrating new classes on the fly in evolving fuzzy classifier designs and their application in visual inspection., , , , and . Appl. Soft Comput., (2015)Self-Adaptive Time-Series Based Forecast Models for Predicting Quality Criteria in Microfluidics Chip Production., , , , , , , and . CYBCONF, page 1-8. IEEE, (2017)Drift detection in data stream classification without fully labelled instances., , , , and . EAIS, page 1-8. IEEE, (2015)