@inproceedings{
 title = {Support vector machine-based approach for multi-labelers problems},
 type = {inproceedings},
 year = {2013},
 websites = {https://www.elen.ucl.ac.be/Proceedings/esann/esannpdf/es2013-118.pdf},
 id = {5f5dfaeb-8892-3cd6-a5e8-15ab07a945c9},
 created = {2022-01-26T03:01:12.382Z},
 file_attached = {false},
 profile_id = {aba9653c-d139-3f95-aad8-969c487ed2f3},
 group_id = {b9022d50-068c-31b4-9174-ebfaaf9ee57b},
 last_modified = {2022-01-26T03:01:12.382Z},
 read = {false},
 starred = {false},
 authored = {false},
 confirmed = {true},
 hidden = {false},
 private_publication = {false},
 abstract = {We propose a first approach to quantify the panelist's labeling generalizing a soft-margin support vector machine classifier to multi-labeler analysis. Our approach consists of formulating a quadratic optimization problem instead of using a heuristic search algorithm. We determine penalty factors for each panelist by incorporating a linear combination in the primal formulation. Solution is obtained on a dual formulation using quadratic programming. For experiments, the well-known Iris with multiple simulated artificial labels and a multi-label speech database are employed. Obtained penalty factors are compared with both standard supervised and non-supervised measurements. Promising results show that proposed method is able to asses the concordance among panelists considering the structure of data.},
 bibtype = {inproceedings},
 author = {Murillo, S. and Peluffo, D.H. and Castellanos, G.},
 booktitle = {ESANN 2013 proceedings, 21st European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning}
}

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