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ICML
2004
IEEE

Online learning of conditionally I.I.D. data

13 years 9 months ago
Online learning of conditionally I.I.D. data
In this work we consider the task of relaxing the i.i.d assumption in online pattern recognition (or classification), aiming to make existing learning algorithms applicable to a wider range of tasks. Online pattern recognition is predicting a sequence of labels based on objects given for each label and on examples (pairs of objects and labels) learned so far. Traditionally, this task is considered under the assumption that examples are independent and identically distributed. However, it turns out that many results of pattern recognition theory carry over under a much weaker assumption. Namely, under the assumption of conditional independence and identical distribution of objects only, while the only condition on the distribution of labels is that the rate of occurrence of each label should be above some positive threshold. We find a broad class of learning algorithms for which estimations of the probability of a classification error achieved under the classical i.i.d. assumption c...
Daniil Ryabko
Added 30 Jun 2010
Updated 30 Jun 2010
Type Conference
Year 2004
Where ICML
Authors Daniil Ryabko
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