@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 = {2e31c347-8ac9-3e66-b4b0-e9814b24a253},
 created = {2020-12-09T05:20:42.419Z},
 file_attached = {false},
 profile_id = {aba9653c-d139-3f95-aad8-969c487ed2f3},
 last_modified = {2021-03-29T22:36:50.766Z},
 read = {false},
 starred = {false},
 authored = {true},
 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}
}

{"_id":"krkhvxRKT7ctcSk6e","bibbaseid":"murillo-peluffo-castellanos-supportvectormachinebasedapproachformultilabelersproblems-2013","authorIDs":[],"author_short":["Murillo, S.","Peluffo, D.","Castellanos, G."],"bibdata":{"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":"2e31c347-8ac9-3e66-b4b0-e9814b24a253","created":"2020-12-09T05:20:42.419Z","file_attached":false,"profile_id":"aba9653c-d139-3f95-aad8-969c487ed2f3","last_modified":"2021-03-29T22:36:50.766Z","read":false,"starred":false,"authored":"true","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","bibtex":"@inproceedings{\n title = {Support vector machine-based approach for multi-labelers problems},\n type = {inproceedings},\n year = {2013},\n websites = {https://www.elen.ucl.ac.be/Proceedings/esann/esannpdf/es2013-118.pdf},\n id = {2e31c347-8ac9-3e66-b4b0-e9814b24a253},\n created = {2020-12-09T05:20:42.419Z},\n file_attached = {false},\n profile_id = {aba9653c-d139-3f95-aad8-969c487ed2f3},\n last_modified = {2021-03-29T22:36:50.766Z},\n read = {false},\n starred = {false},\n authored = {true},\n confirmed = {true},\n hidden = {false},\n private_publication = {false},\n 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.},\n bibtype = {inproceedings},\n author = {Murillo, S. and Peluffo, D.H. and Castellanos, G.},\n booktitle = {ESANN 2013 proceedings, 21st European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning}\n}","author_short":["Murillo, S.","Peluffo, D.","Castellanos, G."],"urls":{"Website":"https://www.elen.ucl.ac.be/Proceedings/esann/esannpdf/es2013-118.pdf"},"biburl":"https://bibbase.org/service/mendeley/53302730-7b0b-3598-84bc-0c824c8d5a8d","bibbaseid":"murillo-peluffo-castellanos-supportvectormachinebasedapproachformultilabelersproblems-2013","role":"author","metadata":{"authorlinks":{}},"downloads":0},"bibtype":"inproceedings","biburl":"https://bibbase.org/service/mendeley/53302730-7b0b-3598-84bc-0c824c8d5a8d","creationDate":"2020-02-26T04:21:14.512Z","downloads":0,"keywords":[],"search_terms":["support","vector","machine","based","approach","multi","labelers","problems","murillo","peluffo","castellanos"],"title":"Support vector machine-based approach for multi-labelers problems","year":2013,"dataSources":["ntXyXv2964fDt3myF","ya2CyA73rpZseyrZ8"]}