{"_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":"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","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 = {5f5dfaeb-8892-3cd6-a5e8-15ab07a945c9},\n created = {2022-01-26T03:01:12.382Z},\n file_attached = {false},\n profile_id = {aba9653c-d139-3f95-aad8-969c487ed2f3},\n group_id = {b9022d50-068c-31b4-9174-ebfaaf9ee57b},\n last_modified = {2022-01-26T03:01:12.382Z},\n read = {false},\n starred = {false},\n authored = {false},\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/aba9653c-d139-3f95-aad8-969c487ed2f3","bibbaseid":"murillo-peluffo-castellanos-supportvectormachinebasedapproachformultilabelersproblems-2013","role":"author","metadata":{"authorlinks":{}}},"bibtype":"inproceedings","biburl":"https://bibbase.org/service/mendeley/aba9653c-d139-3f95-aad8-969c487ed2f3","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"]}