A Lagrangian heuristics for balancing the average weighted completion times of two classes of jobs in a single-machine scheduling problem

Autor: Matteo Avolio, Antonio Fuduli

Publikováno v: EURO Journal on Computational Optimization, Vol 10, Iss , Pp 100032- (2022)

We tackle a new single-machine scheduling problem, whose objective is to balance the average weighted completion times of two classes of jobs. Because both the job sets contribute to the same objective function, this problem can be interpreted as a c

Externí odkaz: https://doaj.org/article/a45759ffc5e2483fb39570865a55e531

A maximum-margin multisphere approach for binary Multiple Instance Learning

Autor: Matteo Avolio, Antonio Fuduli, Annabella Astorino

Publikováno v: European journal of operational research (2021).
info:cnr-pdr/source/autori:Annabella Astorino; Matteo Avolio; Antonio Fuduli/titolo:A maximum-margin multisphere approach for binary Multiple Instance Learning/doi:/rivista:European journal of operational research/anno:2021/pagina_da:/pagina_a:/intervallo_pagine:/volume

We propose a heuristic approach for solving binary Multiple Instance Learning (MIL) problems, whose objective is to categorize bags of instances. Considering the case with two classes of instances, on the basis of the standard MIL assumption, a bag i

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::0c632625b9e3bc176e0b009b7ca59516
https://doi.org/10.1016/j.ejor.2021.11.022

A heuristic approach for multiple instance learning by linear separation

Autor: Antonio Fuduli, Manlio Gaudioso, Walaa Khalaf, Eugenio Vocaturo

Publikováno v: Soft Computing. 26:3361-3368

We present a fast heuristic approach for solving a binary multiple instance learning (MIL) problem, which consists in discriminating between two kinds of item sets: the sets are called bags and the items inside them are called instances. Assuming tha

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::f2109f2857d41db911036ca56f255001
https://doi.org/10.1007/s00500-021-06713-1

Comparing Linear and Spherical Separation Using Grossone-Based Numerical Infinities in Classification Problems

Autor: Annabella Astorino, Antonio Fuduli

Publikováno v: Emergence, Complexity and Computation ISBN: 9783030936419

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::bfbb9f4c2cd2f02cef09a574c9da0055
https://doi.org/10.1007/978-3-030-93642-6_10

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