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Conference Paper: Learning in indefinite proximity spaces - Recent trends
Title | Learning in indefinite proximity spaces - Recent trends |
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Authors | |
Issue Date | 2016 |
Citation | ESANN 2016 - 24th European Symposium on Artificial Neural Networks, 2016, p. 113-122 How to Cite? |
Abstract | Efficient learning of a data analysis task strongly depends on the data representation. Many methods rely on symmetric similarity or dissimilarity representations by means of metric inner products or distances, providing easy access to powerful mathematical formalisms like kernel approaches. Similarities and dissimilarities are however often naturally obtained by non-metric proximity measures which can not easily be handled by classical learning algorithms. Major efforts have been undertaken to provide approaches which can either directly be used for such data or to make standard methods available for these type of data. We provide an overview about recent achievements in the field of learning with indefinite proximities. |
Persistent Identifier | http://hdl.handle.net/10722/341194 |
DC Field | Value | Language |
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dc.contributor.author | Schleif, Frank Michael | - |
dc.contributor.author | Tino, Peter | - |
dc.contributor.author | Liang, Yingyu | - |
dc.date.accessioned | 2024-03-13T08:40:55Z | - |
dc.date.available | 2024-03-13T08:40:55Z | - |
dc.date.issued | 2016 | - |
dc.identifier.citation | ESANN 2016 - 24th European Symposium on Artificial Neural Networks, 2016, p. 113-122 | - |
dc.identifier.uri | http://hdl.handle.net/10722/341194 | - |
dc.description.abstract | Efficient learning of a data analysis task strongly depends on the data representation. Many methods rely on symmetric similarity or dissimilarity representations by means of metric inner products or distances, providing easy access to powerful mathematical formalisms like kernel approaches. Similarities and dissimilarities are however often naturally obtained by non-metric proximity measures which can not easily be handled by classical learning algorithms. Major efforts have been undertaken to provide approaches which can either directly be used for such data or to make standard methods available for these type of data. We provide an overview about recent achievements in the field of learning with indefinite proximities. | - |
dc.language | eng | - |
dc.relation.ispartof | ESANN 2016 - 24th European Symposium on Artificial Neural Networks | - |
dc.title | Learning in indefinite proximity spaces - Recent trends | - |
dc.type | Conference_Paper | - |
dc.description.nature | link_to_subscribed_fulltext | - |
dc.identifier.scopus | eid_2-s2.0-84994086562 | - |
dc.identifier.spage | 113 | - |
dc.identifier.epage | 122 | - |