Ahpatron: A New Budgeted Online Kernel Learning Machine with Tighter Mistake Bound

In this paper, we study the mistake bound of online kernel learning on a budget. We propose a new budgeted online kernel learning model, called Ahpatron, which significantly improves the mistake bound of previous work and resolves the open problem posed by Dekel, Shalev-Shwartz, and Singer (2005). W...

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Hauptverfasser:	Liao, Yun, Li, Junfan, Liao, Shizhong, Hu, Qinghua, Dang, Jianwu
Format:	Artikel
Sprache:	eng
Schlagworte:	Computer Science - Learning Statistics - Machine Learning
Online-Zugang:	Volltext bestellen
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