@inbook{
 type = {inbook},
 year = {2017},
 keywords = {Multi-labeler classification,Supervised kernel,Support vector machines},
 pages = {343-351},
 websites = {http://link.springer.com/10.1007/978-3-319-52277-7_42},
 id = {df7784e7-72e9-3461-a839-1a9bbd3cb4d1},
 created = {2022-01-26T03:00:36.535Z},
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 profile_id = {aba9653c-d139-3f95-aad8-969c487ed2f3},
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 last_modified = {2022-01-26T03:00:36.535Z},
 read = {false},
 starred = {false},
 authored = {false},
 confirmed = {true},
 hidden = {false},
 citation_key = {Imbajoa-Ruiz2017},
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 abstract = {This work introduces a multi-labeler kernel novel approach for data classification learning from multiple labelers. The learning process is done by training support-vector machine classifiers using the set of labelers (one labeler per classifier). The objective functions representing the boundary decision of each classifier are mixed by means of a linear combination. Followed from a variable relevance, the weighting factors are calculated regarding kernel matrices representing each labeler. To do so, a so-called supervised kernel function is also introduced, which is used to construct kernel matrices. Our multi-labeler method reaches very good results being a suitable alternative to conventional approaches.},
 bibtype = {inbook},
 author = {Imbajoa-Ruiz, D. E. and Gustin, I. D. and Bolaños-Ledezma, M. and Arciniegas-Mejía, A. F. and Guasmayan-Guasmayan, F. A. and Bravo-Montenegro, M. J. and Castro-Ospina, A. E. and Peluffo-Ordóñez, D. H.},
 doi = {10.1007/978-3-319-52277-7_42},
 chapter = {Multi-labeler Classification Using Kernel Representations and Mixture of Classifiers},
 title = {Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)}
}

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