A Source Domain Extension Method for Inductive Transfer Learning Based on Flipping Output

A Source Domain Extension Method for Inductive Transfer Learning Based on Flipping Output

Transfer learning aims for high accuracy by applying knowledge of source domains for which data collection is easy in order to target domains where data collection is difficult, and has attracted attention in recent years because of its significant potential to enable the application of machine lear...

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Bibliographic Details
Main Authors: Yasutake Koishi, Shuichi Ishida, Tatsuo Tabaru, Hiroyuki Miyamoto
Format: Article
Language:English
Published: MDPI AG 2019-05-01
Series:Algorithms
Subjects:
transfer learning
ensemble learning
data expansion
flipping output
Online Access:https://www.mdpi.com/1999-4893/12/5/95

Internet

https://www.mdpi.com/1999-4893/12/5/95

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