Imprecise Deep Forest for Partial Label Learning

In partial label (PL) learning, each instance corresponds to a set of candidate labels, among which only one is valid. The objective of PL learning is to obtain a multi-class classifier from the training instances. Because the true label of a PL training instance is hidden in the candidate label set...

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Veröffentlicht in:	IEEE access 2020, Vol.8, p.218530-218541
Hauptverfasser:	Gao, Jie, Lin, Weiping, Liu, Kunhong, Hong, Qingqi, Lin, Guangyi, Wang, Beizhan
Format:	Artikel
Sprache:	eng
Schlagworte:	Algorithms Classification algorithms Classifiers Computer Science Computer Science, Information Systems Convergence Deep forest Deep learning Encoding Engineering Engineering, Electrical & Electronic Error correction error-correcting output codes Forestry Machine learning partial label learning Science & Technology Spatial databases Technology Telecommunications Training
Online-Zugang:	Volltext
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