A novel boosting algorithm for multi-task learning based on the Itakuda-Saito divergence

In this paper, we propose a novel multi-task learning algorithm based on an ensemble learning method. We consider a specific setting of the multi-task learning for binary classification problems, in which features are shared among all tasks and all tasks are targets of performance improvement. We fo...

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Bibliographische Detailangaben

Hauptverfasser:	Takenouchi Takashi, Komori Osamu, Eguchi Shinto
Format:	Tagungsbericht
Sprache:	eng
Schlagworte:	Algorithms Divergence Machine learning
Online-Zugang:	Volltext
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