Bayesian Optimization for Wrapper Feature Selection: Unterschied zwischen den Versionen
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|kurzfassung=... | |kurzfassung=Wrapper feature selection can lead to highly accurate classifications. However, the computational costs for this are very high in general. Bayesian Optimization on the other hand has already proven to be very efficient in optimizing black box functions. This approach uses Bayesian Optimization in order to minimize the number of evaluations, i.e. the training of models with different feature subsets. | ||
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Version vom 29. Mai 2019, 07:42 Uhr
| Vortragende(r) | Adrian Kruck | |
|---|---|---|
| Vortragstyp | Proposal | |
| Betreuer(in) | Jakob Bach | |
| Termin | Fr 7. Juni 2019 | |
| Vortragsmodus | ||
| Kurzfassung | Wrapper feature selection can lead to highly accurate classifications. However, the computational costs for this are very high in general. Bayesian Optimization on the other hand has already proven to be very efficient in optimizing black box functions. This approach uses Bayesian Optimization in order to minimize the number of evaluations, i.e. the training of models with different feature subsets. | |