PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Data Driven Online to Batch Conversions
Ofer Dekel and Yoram Singer
In: NIPS 2005, 5-10 Dec 2005, Vancouver, Canada.


Online learning algorithms are typically fast, memory efficient, and simple to implement. However, many common learning problems fit more naturally in the batch learning setting. The power of online learning algorithms can be exploited in batch settings by using \emph{online-to-batch} conversions, techniques which build a new batch algorithm from an existing online algorithm. We first give a unified overview of three existing online-to-batch conversion techniques which do not use training data in the conversion process. We then build upon these data-independent conversions to derive and analyze data-driven conversions. Our conversions find hypotheses with a small risk by explicitly minimizing data-dependent generalization bounds. We experimentally demonstrate the usefulness of our approach, and in particular show that the data-driven conversions consistently outperform the data-independent conversions.

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EPrint Type:Conference or Workshop Item (Paper)
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Theory & Algorithms
ID Code:1647
Deposited By:Ofer Dekel
Deposited On:28 November 2005