Maximum-margin polyhedral separation for binary Multiple Instance Learning

Autor: Annabella Astorino, Matteo Avolio, Antonio Fuduli

Publikováno v: EURO Journal on Computational Optimization, Vol 11, Iss , Pp 100070- (2023)

Multiple Instance Learning (MIL) is a kind of weak supervised learning, where each sample is represented by a bag of instances. The main characteristic of such problems resides in the training phase, since the class labels are provided only for each

Externí odkaz: https://doaj.org/article/2b68c3ba229c4c6c8395ca3d9cf8e88c

Partitional clustering via successive transportation problems

Autor: Annabella Astorino, Matteo Avolio, Annamaria Canino, Teresa Crupi, Antonio Fuduli

Publikováno v: Operations Research Letters. 51:40-46

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::8b62820a02ed93fd9da6e026650faa74
https://doi.org/10.1016/j.orl.2022.11.008

Polyhedral separation via difference of convex (DC) programming

Autor: Annabella Astorino, Massimo Di Francesco, Manlio Gaudioso, Enrico Gorgone, Benedetto Manca

Publikováno v: Soft computing (Berl., Print) (2021). doi:10.1007/s00500-021-05758-6
info:cnr-pdr/source/autori:Annabella Astorino; Massimo Di Francesco; Manlio Gaudioso; Enrico Gorgone; Benedetto Manca/titolo:Polyhedral separation via difference of convex (DC) programming/doi:10.1007%2Fs00500-021-05758-6/rivista:Soft computing (Berl., Print)/anno:2021/pagina_da:/pagina_a:/intervallo_pagine:/volume

We consider polyhedral separation of sets as a possible tool in supervised classification. In particular, we focus on the optimization model introduced by Astorino and Gaudioso (J Optim Theory Appl 112(2):265–293, 2002) and adopt its reformulation

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::0d8920fb736ed38d2dfecbbf18b63fb1
https://doi.org/10.1007/s00500-021-05758-6

A Lagrangian Relaxation Approach for Binary Multiple Instance Classification

Autor: Manlio Gaudioso, Annabella Astorino, Antonio Fuduli

Publikováno v: IEEE Transactions on Neural Networks and Learning Systems 30 (2019): 2662–2671. doi:10.1109/TNNLS.2018.2885852
info:cnr-pdr/source/autori:Annabella Astorino; Antonio Fuduli; Manlio Gaudioso./titolo:A Lagrangian Relaxation Approach for Binary Multiple Instance Classification/doi:10.1109%2FTNNLS.2018.2885852/rivista:IEEE Transactions on Neural Networks and Learning Systems/anno:2019/pagina_da:2662/pagina_a:2671/intervallo_pagine:2662–2671/volume:30
LION11, The 2017 Learning and Intelligent Optimization Conference, Nizhny Novgorod, Russia, June 19-21, 2017
info:cnr-pdr/source/autori:Annabella Astorino; Antonio Fuduli; Manlio Gaudioso/congresso_nome:LION11, The 2017 Learning and Intelligent Optimization Conference/congresso_luogo:Nizhny Novgorod, Russia/congresso_data:June 19-21, 2017/anno:2017/pagina_da:/pagina_a:/intervallo_pagine

In the standard classification problems, the objective is to categorize points into different classes. Multiple instance learning (MIL), instead, is aimed at classifying bags of points , each point being an instance . The main peculiarity of a MIL pr

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::c24f5c3c01d7b6249ca32947b49eef6e
https://doi.org/10.1109/tnnls.2018.2885852

A Lagrangean relaxation approach to lifetime maximization of directional sensor networks

Autor: Giovanna Miglionico, Annabella Astorino, Manlio Gaudioso

Publikováno v: Networks (N.Y.N.Y., Print) (2021). doi:10.1002/net.22017
info:cnr-pdr/source/autori:Astorino, Annabella; Gaudioso, Manlio; Miglionico, Giovanna/titolo:A Lagrangean relaxation approach to lifetime maximization of directional sensor networks/doi:10.1002%2Fnet.22017/rivista:Networks (N.Y.N.Y., Print)/anno:2021/pagina_da:/pagina_a:/intervallo_pagine:/volume

We consider the directional sensor network lifetime maximization problem (DSLMP). Given a set of directional sensor and target locations, the problem consists in assigning, at each time unit of a given time horizon, the action radius, the aperture an

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::7aad3fdd236d26238fa0ec2c93791290