Learning algorithms consider a sample consisting of pairs (pattern, label) and output a decision rule, possibly: (i) associating each pattern with the corresponding label, and (ii) generalizing to new patterns drawn from the same distribution of the original sample. This work proposes a set of methodologies to be applied to existing learning strategies in order to deal with more complex kinds of data sets, carrying also a quantitative measure on the quality of each label.
Embedding sample points uncertainty measures in learning algorithms / D. Malchiodi. - In: NONLINEAR ANALYSIS. - ISSN 1751-570X. - 2:2(2008 Jun), pp. 635-647. [10.1016/j.nahs.2006.12.004]
Embedding sample points uncertainty measures in learning algorithms
D. MalchiodiPrimo
2008
Abstract
Learning algorithms consider a sample consisting of pairs (pattern, label) and output a decision rule, possibly: (i) associating each pattern with the corresponding label, and (ii) generalizing to new patterns drawn from the same distribution of the original sample. This work proposes a set of methodologies to be applied to existing learning strategies in order to deal with more complex kinds of data sets, carrying also a quantitative measure on the quality of each label.
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