Meta-learning for Encoder Selection: Unterschied zwischen den Versionen
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|kurzfassung= | |kurzfassung=In the real world, mixed-type data is commonly used, which means it contains both categorical and numerical data. However, most algorithms can only learn from numerical data. This makes the selection of encoder becoming very important. In this presentation, I will present an approach by using ideas from meta-learning to predict the performance from the meta-features and encoders. | ||
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Aktuelle Version vom 21. Juni 2022, 08:35 Uhr
| Vortragende(r) | Mingzhe Tao | |
|---|---|---|
| Vortragstyp | Proposal | |
| Betreuer(in) | Federico Matteucci | |
| Termin | Fr 24. Juni 2022 | |
| Vortragssprache | ||
| Vortragsmodus | in Präsenz | |
| Kurzfassung | In the real world, mixed-type data is commonly used, which means it contains both categorical and numerical data. However, most algorithms can only learn from numerical data. This makes the selection of encoder becoming very important. In this presentation, I will present an approach by using ideas from meta-learning to predict the performance from the meta-features and encoders. | |