Consistent and Conservative Iterative Learning

dc.contributor.authorJAIN, Sanjayen_US
dc.contributor.authorLANGE, Steffenen_US
dc.contributor.authorZILLES, Sandraen_US
dc.date.accessioned2007-03-21T01:32:49Zen_US
dc.date.accessioned2017-01-23T06:59:55Z
dc.date.available2007-03-21T01:32:49Zen_US
dc.date.available2017-01-23T06:59:55Z
dc.date.issued2007-03-21T01:32:49Zen_US
dc.description.abstractThe present study aims at insights into the nature of incremental learning in the context of Gold's model of identification in the limit. With a focus on natural requirements such as consistency and conservativeness, incremental learning is analysed both for learning from positive examples and for learning from positive and negative examples. The results obtained illustrate in which way different consistency and conservativeness demands can affect the capabilities of incremental learners. These results may serve as a first step towards characterising the structure of typical classes learnable incrementally and thus towards elaborating uniform incremental learning methods.en_US
dc.format.extent527706 bytesen_US
dc.format.mimetypeapplication/pdfen_US
dc.identifier.urihttps://dl.comp.nus.edu.sg/xmlui/handle/1900.100/2317en_US
dc.language.isoenen_US
dc.relation.ispartofseriesTRC3/07en_US
dc.titleConsistent and Conservative Iterative Learningen_US
dc.typeTechnical Reporten_US
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