Research
Research areas
- Data-driven induction of fuzzy sets
- Compression of machine learning models
- Mining of knowledge bases in semantic Web
- Negative example selection in bioinformatics
- ML-based COVID-19 risk prediction
- Application of ML in veterinary and forensics
- Popularization of informatics culture
All areasProjects
All projectsPublications
[Galizzi et al., 2021] |
Galizzi A., Bagardi M., Stranieri A., Zanaboni A. Maria, Malchiodi D., Borromeo V., Brambilla P. G.
and
Locatelli C. Factors affecting the urinary aldosterone-to-creatinine ratio in healthy dogs and dogs with naturally occurring myxomatous mitral valve disease,
BMC Veterinary Research 17
- 1
(2021),
1—14
[doi> BIBTEX]
|
[Marinò et al., 2021] |
Marinò G., Ghidoli G., Frasca M.
and
Malchiodi D. Reproducing the sparse Huffman Address Map compression for deep neural networks, in
B. Kerautret, M. Colom, A. Krähenbühl, D. Lopresti, P. Monasse
and
H. Talbot
(Eds.),
Proceedings of the third Workshop on Reproducible Research in Pattern Recognition RRPR2021,
Springer, Lecture Notes in Computer Science,
2021,
in press
[BIBTEX]
|
[Marinò et al., 2021a] |
Marinò G. C., Ghidoli G., Frasca M.
and
Malchiodi D. Compression strategies and space-conscious representations for deep neural networks, in
Proceedings of the 25th International Conference on Pattern Recognition (ICPR2020),
IEEE,
9835—9842,
2021
[BIBTEX]
|
[Casiraghi et al., 2020] |
Casiraghi E., Malchiodi D., Trucco G., Frasca M., Cappelletti L., Fontana T., Esposito A. A., Avola E., Jachetti A., Reese J., Rizzi A., Robinson P. N.
and
Valentini G. Explainable machine learning for early assessment of COVID-19 risk prediction in emergency departments,
IEEE Access 8
(2020),
196299—196325
[doi> BIBTEX]
|
[Malchiodi et al., 2020] |
Malchiodi D., da Costa Pereira C.
and
Tettamanzi A. G. B. Classifying Candidate Axioms via Dimensionality Reduction Techniques, in
V. Torra, Y. Narukawa, J. Nin
and
N. Agell
(Eds.),
Modeling Decisions for Artificial Intelligence. 17th International Conference, MDAI 2020 Sant Cugat, Spain, September 2–4, 2020 Proceedings,
Cham, Switzerland:
Springer, Lecture Notes in Computer Sciencce 12256,
179—191,
2020
[doi> BIBTEX]
|
[Cermenati et al., 2020] |
Cermenati L., Malchiodi D.
and
Zanaboni A. Simultaneous Learning of Fuzzy Sets, in
A. Esposito, M. Faundez-Zanuy, M. Morabito
and
E. Pasero
(Eds.),
Neural Approaches to Dynamics of Signal Exchanges,
Vol. 151,
Singapore:
Springer, Smart Innovation, Systems and Technologies,
167-175,
2020
[doi> BIBTEX]
|
[Lodi et al., 2019] |
Lodi M., Malchiodi D., Monga M., Morpurgo A.
and
Spieler B. Constructionist Attempts at Supporting the Learning of Computer Programming: A Survey,
Olympiads in Informatics 13
(2019),
99—121
[doi> BIBTEX]
|
[Malchiodi and Zanaboni, 2019] |
Malchiodi D.
and
Zanaboni A. Data-Driven Induction of Shadowed Sets Based on Grade of Fuzziness, in
R. Fullér, S. Giove
and
F. Masulli
(Eds.),
Fuzzy Logic and Applications — 12th International Workshop, WILF 2018 Genoa, Italy, September 6–7, 2018 — Revised Selected Papers,
Cham:
Springer Nature Switzerland AG, Lecture Notes in Artificial Intelligence 11291 (ISBN 978-3-030-12543-1/978-3-030-12544-8),
17—28,
2019
[doi> BIBTEX]
|
[Malchiodi et al., 2018] |
Malchiodi D., da Costa Pereira C.
and
Tettamanzi A. G. B. Predicting the Possibilistic Score of OWL Axioms through Support Vector Regression, in
D. Ciucci, G. Pasi
and
B. Vantaggi
(Eds.),
Scalable Uncertainty Management. SUM 2018,
Cham:
Springer, Lecture Notes in Artificial Intelligence 11142 (ISBN 978-3-030-00460-6/978-3-030-00461-3),
2018
[doi> BIBTEX]
|
[Boldi et al., 2018] |
Boldi P., Frasca M.
and
Malchiodi D. Evaluating the impact of topological protein features on the negative examples selection,
BMC Bioinformatics 19
- 14
(2018),
417.115–417.126
[doi> Open access link BIBTEX]
|
[Malchiodi and Tettamanzi, 2018] |
Malchiodi D.
and
Tettamanzi A. G. B. Predicting the Possibilistic Score of OWL Axioms through Modified Support Vector Clustering, in
H. Haddad, R. L. Wainwright
and
R. Chbeir
(Eds.),
SAC'18: Proceedings of the 33rd Annual ACM Symposium on Applied Computing,
ACM (ISBN 9781450351911),
1984–1991,
2018
[doi> BIBTEX]
|
[Malchiodi and Pedrycz, 2013] |
Malchiodi D.
and
Pedrycz W. Learning Membership Functions for Fuzzy Sets through Modified Support Vector Clustering, in
F. Masulli, G. Pasi
and
R. Yager
(Eds.),
Fuzzy Logic and Applications. 10th International Workshop, WILF 2013, Genoa, Italy, November 19–22, 2013. Proceedings.,
Vol. 8256,
Springer International Publishing, Switzerland, Lecture Notes on Artificial Intelligence (ISBN 978-3-319-03199-6),
52–59,
2013
[doi> BIBTEX]
|
All publications