Online learning algorithms are typically fast, memory ef\ufb01cient, and simple to implement. However, many common learning problems \ufb01t more naturally in the batch learning setting. The power of online learning algorithms can be exploited in batch settings by using online-to-batch conversions techniques which build a new batch algorithm from an existing online algorithm. We \ufb01rst give a uni\ufb01ed 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 \ufb01nd hypotheses with a small risk by explicitly minimizing datadependent 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.<\/p>\n","protected":false},"excerpt":{"rendered":"
Online learning algorithms are typically fast, memory ef\ufb01cient, and simple to implement. However, many common learning problems \ufb01t more naturally in the batch learning setting. The power of online learning algorithms can be exploited in batch settings by using online-to-batch conversions techniques which build a new batch algorithm from an existing online algorithm. 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