https://sdq.kastel.kit.edu/api.php?action=feedcontributions&user=Uferh&feedformat=atomSDQ-Institutsseminar - Benutzerbeiträge [de]2024-03-28T10:13:31ZBenutzerbeiträgeMediaWiki 1.39.6https://sdq.kastel.kit.edu/mediawiki-institutsseminar/index.php?title=Development_of_an_Active_Learning_Approach_for_One_Class_Classifi_cation_using_Bayesian_Uncertainty&diff=2192Development of an Active Learning Approach for One Class Classifi cation using Bayesian Uncertainty2022-05-18T09:48:58Z<p>Uferh: Die Seite wurde neu angelegt: „{{Vortrag |vortragender=Tobias Haßberg |email=uferh@student.kit.edu |vortragstyp=Masterarbeit |betreuer=Bela Böhnke |termin=Institutsseminar/2022-06-03 |vort…“</p>
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<div>{{Vortrag<br />
|vortragender=Tobias Haßberg<br />
|email=uferh@student.kit.edu<br />
|vortragstyp=Masterarbeit<br />
|betreuer=Bela Böhnke<br />
|termin=Institutsseminar/2022-06-03<br />
|vortragsmodus=in Präsenz<br />
|kurzfassung=In One-Class classification, the classifier decides if points belong to a specific class. In this thesis, we propose an One-Class classification approach, suitable for active learning, that models for each point, a prediction range in which the model assumes the points state to be. The proposed classifier uses a Gaussian process. We use the Gaussian processes prediction range to derive a certainty measure, that considers the available labeled points for stating its certainty. We compared this approach against baseline classifiers and show the correlation between the classifier's uncertainty and misclassification ratio.<br />
}}</div>Uferhhttps://sdq.kastel.kit.edu/mediawiki-institutsseminar/index.php?title=Development_of_an_Active_Learning_Approach_for_One_Class_Classi%EF%AC%81cation_using_Bayesian_Uncertainty&diff=1846Development of an Active Learning Approach for One Class Classification using Bayesian Uncertainty2021-11-01T13:45:10Z<p>Uferh: </p>
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<div>{{Vortrag<br />
|vortragender=Tobias Haßberg<br />
|email=uferh@student.kit.edu<br />
|vortragstyp=Proposal<br />
|betreuer=Bela Böhnke<br />
|termin=Institutsseminar/2021-11-05<br />
|kurzfassung=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. <br />
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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.<br />
}}</div>Uferhhttps://sdq.kastel.kit.edu/mediawiki-institutsseminar/index.php?title=Development_of_an_Active_Learning_Approach_for_One_Class_Classi%EF%AC%81cation_using_Bayesian_Uncertainty&diff=1845Development of an Active Learning Approach for One Class Classification using Bayesian Uncertainty2021-10-31T16:19:59Z<p>Uferh: Die Seite wurde neu angelegt: „{{Vortrag |vortragender=Tobias Haßberg |email=uferh@student.kit.edu |vortragstyp=Proposal |betreuer=Bela Böhnke |termin=Institutsseminar/2021-11-05 |kurzfass…“</p>
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<div>{{Vortrag<br />
|vortragender=Tobias Haßberg<br />
|email=uferh@student.kit.edu<br />
|vortragstyp=Proposal<br />
|betreuer=Bela Böhnke<br />
|termin=Institutsseminar/2021-11-05<br />
|kurzfassung=TBD.<br />
}}</div>Uferhhttps://sdq.kastel.kit.edu/mediawiki-institutsseminar/index.php?title=Bela_B%C3%B6hnke&diff=1844Bela Böhnke2021-10-31T16:19:19Z<p>Uferh: Die Seite wurde neu angelegt: „{{Betreuer |email=bela.boehnke@kit.edu |homepage=https://dbis.ipd.kit.edu/2982.php |lehrstuhl=IPD Böhm }}“</p>
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<div>{{Betreuer<br />
|email=bela.boehnke@kit.edu<br />
|homepage=https://dbis.ipd.kit.edu/2982.php<br />
|lehrstuhl=IPD Böhm<br />
}}</div>Uferh