Meta-Modeling the Feature Space: Unterschied zwischen den Versionen
(Die Seite wurde neu angelegt: „{{Vortrag |vortragender=Patrick Ehrler |email=patrick.ehrler@student.kit.edu |vortragstyp=Proposal |betreuer=Jakob Bach |termin=Institutsseminar/2020-12-11 |ku…“) |
Keine Bearbeitungszusammenfassung |
||
| Zeile 5: | Zeile 5: | ||
|betreuer=Jakob Bach | |betreuer=Jakob Bach | ||
|termin=Institutsseminar/2020-12-11 | |termin=Institutsseminar/2020-12-11 | ||
|kurzfassung= | |kurzfassung=Feature Selection is an important process in Machine Learning to improve model training times and complexity. One state-of-the art approach is Wrapper Feature Selection where subsets of features are evaluated. Because we can not evaluate all 2^n subsets an appropriate search strategy is vital. | ||
Bayesian Optimization has already been successfully used in the context of hyperparameter optimization and very specific Feature Selection contexts. We want to look on how to use Bayesian Optimization for Feature Selection and discuss its limitations and possible solutions. | |||
}} | }} | ||
Aktuelle Version vom 7. Dezember 2020, 16:27 Uhr
| Vortragende(r) | Patrick Ehrler | |
|---|---|---|
| Vortragstyp | Proposal | |
| Betreuer(in) | Jakob Bach | |
| Termin | Fr 11. Dezember 2020 | |
| Vortragssprache | ||
| Vortragsmodus | ||
| Kurzfassung | Feature Selection is an important process in Machine Learning to improve model training times and complexity. One state-of-the art approach is Wrapper Feature Selection where subsets of features are evaluated. Because we can not evaluate all 2^n subsets an appropriate search strategy is vital.
Bayesian Optimization has already been successfully used in the context of hyperparameter optimization and very specific Feature Selection contexts. We want to look on how to use Bayesian Optimization for Feature Selection and discuss its limitations and possible solutions. | |