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Abstract: In this work we consider the task of relaxing the i.i.d. assumption in pattern recognition (or classification) , aiming to make existing learning algorithms applicable to a wider range of tasks. Pattern recognition is guessing a discrete label of some object based on a set of given examples (pairs of objects and labels). We consider the case of deterministically defined labels. Traditionally, this task is studied under the assumption that examples are independent and identically... (Update)
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BibTeX entry: (Update)
@misc{ daniil-journal,
author = "Daniil Ryabko Daniil",
title = "Journal of Machine Learning Research 7 (2006) 645--664 Submitted 7/05;
Revised 12/05; Published 5/06 Pattern Recognition for Conditionally Independent
Data",
url = "citeseer.ist.psu.edu/761140.html" }
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