|Datum||Fr 5. November 2021, 11:30 Uhr|
|Vorheriger Termin||Fr 29. Oktober 2021|
|Nächster Termin||Fr 5. November 2021|
|Titel||Development of an Active Learning Approach for One Class Classiﬁcation using Bayesian Uncertainty|
|Kurzfassung||HYBRID: This Proposal will be online AND in the seminar room 348.
When working with large data sets, in many situations one has to deals with a large set data from a single class and only few negative examples from other classes. Learning classifiers, which can assign data points to one of the groups, is known as one-class classification (OCC) or outlier detection.
The objective of this thesis is to develop and evaluate an active learning process to train an OCC. The process uses domain knowledge to reasonably adopt a prior distribution. Knowing that prior distribution, query strategies will be evaluated, which consider the certainty, more detailed the uncertainty, of the estimated class membership scorings. The integration of the prior distribution and the estimation of uncertainty, will be modeled using a gaussian process.
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