Local Rademacher Complexity: sharper risk bounds with and without unlabeled samples.
We derive in this paper a new Local Rademacher Complexity risk bound on the generalization ability of a model, which is able to take advantage of the availability of unlabeled samples. Moreover, this new bound improves state-of-the-art results even when no unlabeled samples are available.
DITEN - University of Genova, Via Opera Pia 11A, I-16145 Genova, Italy. Electronic address: Luca.Oneto@unige.it.
Luca
L
Oneto
DIBRIS - University of Genova, Via Opera Pia 13, I-16145 Genova, Italy. Electronic address: Alessandro.Ghio@unige.it.
Alessandro
A
Ghio
DITEN - University of Genova, Via Opera Pia 11A, I-16145 Genova, Italy. Electronic address: Sandro.Ridella@unige.it.
Sandro
S
Ridella
DIBRIS - University of Genova, Via Opera Pia 13, I-16145 Genova, Italy. Electronic address: Davide.Anguita@unige.it.
Davide
D
Anguita
D001185:Artificial Intelligence; D015233:Models, Statistical
Local Rademacher Complexity; Performance estimation; Statistical learning theory; Unlabeled samples