Machine Learning, Optimization, and Data Science: 6th International Conference, LOD 2020, Siena, Italy, July 19–23, 2020, Revised Selected Papers, Part I | SpringerLink
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Machine Learning, Optimization, and Data Science

6th International Conference, LOD 2020, Siena, Italy, July 19–23, 2020, Revised Selected Papers, Part I

  • Conference proceedings
  • © 2020

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Editors:
  1. Giuseppe Nicosia

    1. University of Catania, Catania, Italy

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  2. Varun Ojha

    1. University of Reading, Reading, UK

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  3. Emanuele La Malfa

    1. University of Oxford, Oxford, UK

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  4. Giorgio Jansen

    1. University of Cambridge, Cambridge, UK

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  5. Vincenzo Sciacca

    1. Almawave, Rome, Italy

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  6. Panos Pardalos

    1. University of Florida, Gainesville, USA

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  7. Giovanni Giuffrida

    1. University of Catania, Catania, Italy

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  8. Renato Umeton

    1. Harvard University, Cambridge, USA

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Part of the book series: Lecture Notes in Computer Science (LNCS, volume 12565)

Part of the book sub series: Information Systems and Applications, incl. Internet/Web, and HCI (LNISA)

Included in the following conference series:

Conference proceedings info: LOD 2020.

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About this book

This two-volume set, LNCS 12565 and 12566, constitutes the refereed proceedings of the 6th International Conference on Machine Learning, Optimization, and Data Science, LOD 2020, held in Siena, Italy, in July 2020.

The total of 116 full papers presented in this two-volume post-conference proceedings set was carefully reviewed and selected from 209 submissions. These research articles were written by leading scientists in the fields of machine learning, artificial intelligence, reinforcement learning, computational optimization, and data science presenting a substantial array of ideas, technologies, algorithms, methods, and applications.

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Table of contents (64 papers)

  1. Front Matter

    Pages i-xxxvii
    Download chapter PDF

  2. Revisiting Clustering as Matrix Factorisation on the Stiefel Manifold

    • Stéphane Chrétien, Benjamin Guedj
    Pages 1-12

  3. A Generalized Quadratic Loss for SVM and Deep Neural Networks

    • Filippo Portera
    Pages 13-24

  4. Machine Learning Application to Family Business Status Classification

    • Giorgio Gnecco, Stefano Amato, Alessia Patuelli, Nicola Lattanzi
    Pages 25-36

  5. Using Hessians as a Regularization Technique

    • Adel Rahimi, Tetiana Kodliuk, Othman Benchekroun
    Pages 37-40

  6. Scaling Up Quasi-newton Algorithms: Communication Efficient Distributed SR1

    • Majid Jahani, Mohammadreza Nazari, Sergey Rusakov, Albert S. Berahas, Martin Takáč
    Pages 41-54

  7. Should Simplicity Be Always Preferred to Complexity in Supervised Machine Learning?

    • Falco Bargagli-Stoffi, Gustavo Cevolani, Giorgio Gnecco
    Pages 55-59

  8. An Application of Machine Learning to Study Utilities Expenses in the Brazilian Navy

    • Stefan Silva, José Crispim
    Pages 60-71

  9. The Role of Animal Spirit in Monitoring Location Shifts with SVM:Novelties Versus Outliers

    • Iulia Igescu
    Pages 72-82

  10. Long-Term Prediction of Physical Interactions: A Challenge for Deep Generative Models

    • Alberto Cenzato, Alberto Testolin, Marco Zorzi
    Pages 83-94

  11. Semantic Segmentation of Neuronal Bodies in Fluorescence Microscopy Using a 2D+3D CNN Training Strategy with Sparsely Annotated Data

    • Filippo M. Castelli, Matteo Roffilli, Giacomo Mazzamuto, Irene Costantini, Ludovico Silvestri, Francesco S. Pavone
    Pages 95-99

  12. Methods for Hyperparameters Optimization in Learning Approaches: An Overview

    • Nicoletta Del Buono, Flavia Esposito, Laura Selicato
    Pages 100-112

  13. Novel Reconstruction Errors for Saliency Detection in Hyperspectral Images

    • Antonella Falini, Cristiano Tamborrino, Graziano Castellano, Francesca Mazzia, Rosa Maria Mininni, Annalisa Appice et al.
    Pages 113-124

  14. Sparse Consensus Classification for Discovering Novel Biomarkers in Rheumatoid Arthritis

    • Cláudia Constantino, Alexandra M. Carvalho, Susana Vinga
    Pages 125-136

  15. Learning More Expressive Joint Distributions in Multimodal Variational Methods

    • Sasho Nedelkoski, Mihail Bogojeski, Odej Kao
    Pages 137-149

  16. Estimating the \(F_1\) Score for Learning from Positive and Unlabeled Examples

    • Seyed Amin Tabatabaei, Jan Klein, Mark Hoogendoorn
    Pages 150-161

  17. Dynamic Industry-Specific Lexicon Generation for Stock Market Forecast

    • Salvatore Carta, Sergio Consoli, Luca Piras, Alessandro Sebastian Podda, Diego Reforgiato Recupero
    Pages 162-176

  18. A Stochastic Optimization Model for Frequency Control and Energy Management in a Microgrid

    • Dhekra Bousnina, Welington de Oliveira, Peter Pflaum
    Pages 177-189

  19. Using the GDELT Dataset to Analyse the Italian Sovereign Bond Market

    • Sergio Consoli, Luca Tiozzo Pezzoli, Elisa Tosetti
    Pages 190-202Open Access

  20. Adjusted Measures for Feature Selection Stability for Data Sets with Similar Features

    • Andrea Bommert, Jörg Rahnenführer
    Pages 203-214
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Other volumes

  1. Machine Learning, Optimization, and Data Science

  2. Machine Learning, Optimization, and Data Science

Editors and Affiliations

  • University of Catania, Catania, Italy

    Giuseppe Nicosia, Giovanni Giuffrida

  • University of Reading, Reading, UK

    Varun Ojha

  • University of Oxford, Oxford, UK

    Emanuele La Malfa

  • University of Cambridge, Cambridge, UK

    Giorgio Jansen

  • Almawave, Rome, Italy

    Vincenzo Sciacca

  • University of Florida, Gainesville, USA

    Panos Pardalos

  • Harvard University, Cambridge, USA

    Renato Umeton

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