Development of an Active Learning Approach for One Class Classification using Bayesian Uncertainty: Unterschied zwischen den Versionen

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|betreuer=Bela Böhnke
 
|betreuer=Bela Böhnke
 
|termin=Institutsseminar/2021-11-05 Zusatztermin
 
|termin=Institutsseminar/2021-11-05 Zusatztermin
|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.  
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|kurzfassung=HYBRID: This Proposal will be online AND in the seminar room 348.
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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.
 
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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Aktuelle Version vom 2. November 2021, 14:14 Uhr

Vortragende(r) Tobias Haßberg
Vortragstyp Proposal
Betreuer(in) Bela Böhnke
Termin Fr 5. November 2021
Vortragsmodus
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.