Resource Efficient Representation of Machine Learning Models : investigating optimization options for decision trees in embedded systems
Combining embedded systems and machine learning models is an exciting prospect. However, to fully target any embedded system, with the most stringent resource requirements, the models have to be designed with care not to overwhelm it. Decision tree ensembles are targeted in this thesis. A benchmark...
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| Format: | Others |
| Language: | English |
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Linköpings universitet, Statistik och maskininlärning
2019
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| Online Access: | http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-162013 |