Meta-learning for Encoder Selection

Aus SDQ-Institutsseminar
Version vom 21. Juni 2022, 09:35 Uhr von Federico Matteucci (Diskussion | Beiträge)
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Vortragende(r) Mingzhe Tao
Vortragstyp Proposal
Betreuer(in) Federico Matteucci
Termin Fr 24. Juni 2022
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.