L
Luca Oneto
Researcher at University of Genoa
Publications - 195
Citations - 7058
Luca Oneto is an academic researcher from University of Genoa. The author has contributed to research in topics: Computer science & Support vector machine. The author has an hindex of 29, co-authored 169 publications receiving 5046 citations. Previous affiliations of Luca Oneto include University of Pisa.
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Book ChapterDOI
ReForeSt: Random Forests in Apache Spark
TL;DR: This work presents ReForeSt, a Random Forests Apache Spark implementation which is easier to tune, faster, and less memory consuming with respect to MLlib, the de facto standard Apache Spark machine learning library.
Journal ArticleDOI
Multilayer Graph Node Kernels: Stacking While Maintaining Convexity
TL;DR: A new family of kernels for graphs which exploits an abstract representation of the information inspired by the multilayer perceptron architecture through a series of stacked kernel pre-image estimators, trained in an unsupervised fashion via convex optimization.
Book ChapterDOI
Innovation capability of firms: a big data approach with patents
TL;DR: Results show that the most important patent’s features useful to predict IC refer to the specific technological areas, the backward citations, the technological domains and the family size.
Journal ArticleDOI
Model scale cavitation noise spectra prediction: Combining physical knowledge with data science
TL;DR: In this paper, a hybrid approach is proposed to predict the cavitation noise spectra without requiring an actual test in a cavitation tunnel with a model of the propeller, which can be used to overcome typical model scale problems.
Book ChapterDOI
Marine safety and data analytics : vessel crash stop maneuvering performance prediction
Luca Oneto,Andrea Coraddu,Paolo Sanetti,Olena Karpenko,Francesca Cipollini,Toine Cleophas,Davide Anguita +6 more
TL;DR: A new data-driven method, based on the popular Random Forests learning algorithm, for predicting the crash stopping maneuvering performance is proposed, which shows the effectiveness of the proposal on real-world data provided by the DAMEN Shipyards.