Clustering and semi-supervised classification with application to driver distraction detection
Clustering and Semi-Supervised Classification (SSC) algorithms can make use of unlabeled training data and thus have the potential to alleviate labeling costs. For example, Extreme Learning Machine (ELM) was recently extended to semi-supervised learning and clustering with promising performance. Mea...
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| 格式: | Thesis |
| 语言: | English |
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2018
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| 在线阅读: | https://hdl.handle.net/10356/89229 http://hdl.handle.net/10220/46179 |