Bayesian optimization for selecting training and validation data for supervised machine learning : using Gaussian processes both to learn the relationship between sets of training data and model performance, and to estimate model performance over the entire problem domain
Validation and verification in machine learning is an open problem which becomes increasingly important as its applications becomes more critical. Amongst the applications are autonomous vehicles and medical diagnostics. These systems all needs to be validated before being put into use or else the c...
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| Format: | Others |
| Language: | English |
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Linköpings universitet, Artificiell intelligens och integrerade datorsystem
2019
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| Online Access: | http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-157327 |