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Identifying the Machine Learning Family from Black-Box Models

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Identifying the Machine Learning Family from Black-Box Models

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Fabra-Boluda, R.; Ferri Ramírez, C.; Hernández-Orallo, J.; Martínez-Plumed, F.; Ramírez Quintana, MJ. (2018). Identifying the Machine Learning Family from Black-Box Models. Lecture Notes in Computer Science. 11160:55-65. https://doi.org/10.1007/978-3-030-00374-6_6

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Título: Identifying the Machine Learning Family from Black-Box Models
Autor: Fabra-Boluda, Raúl Ferri Ramírez, César Hernández-Orallo, José Martínez-Plumed, Fernando Ramírez Quintana, María José
Entidad UPV: Universitat Politècnica de València. Departamento de Sistemas Informáticos y Computación - Departament de Sistemes Informàtics i Computació
Fecha difusión:
Resumen:
[EN] We address the novel question of determining which kind of machine learning model is behind the predictions when we interact with a black-box model. This may allow us to identify families of techniques whose models ...[+]
Palabras clave: Machine learning families , Black-box model , Dissimilarity measures , Adversarial machine learning
Derechos de uso: Reserva de todos los derechos
Fuente:
Lecture Notes in Computer Science. (issn: 0302-9743 )
DOI: 10.1007/978-3-030-00374-6_6
Editorial:
Springer-Verlag
Versión del editor: https://doi.org/10.1007/978-3-030-00374-6_6
Título del congreso: XVIII Conferencia de la Asociación Española para la Inteligencia Artificial (CAEPIA'18)
Lugar del congreso: Granada, España
Fecha congreso: Octubre 23-26,2018
Código del Proyecto:
info:eu-repo/grantAgreement/MECD//PRX17%2F00467/
...[+]
info:eu-repo/grantAgreement/MECD//PRX17%2F00467/
info:eu-repo/grantAgreement/GVA//BEST%2F2017%2F045/
info:eu-repo/grantAgreement/GVA//PROMETEOII%2F2015%2F013/ES/SmartLogic: Logic Technologies for Software Security and Performance/
info:eu-repo/grantAgreement/MINECO//TIN2015-69175-C4-1-R/ES/SOLUCIONES EFECTIVAS BASADAS EN LA LOGICA/
info:eu-repo/grantAgreement/AFOSR//FA9550-17-1-0287/US/Who's behind these predictions? Reconciling transparency and privacy in machine learning/
info:eu-repo/grantAgreement/INCIBE//INCIBEI-2015-27345/
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Agradecimientos:
This material is based upon work supported by the Air Force Office of Scientific Research under award number FA9550-17-1-0287, the EU (FEDER), and the Spanish MINECO under grant TIN 2015-69175-C4-1-R, the Generalitat ...[+]
Tipo: Artículo Comunicación en congreso Capítulo de libro

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