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
The Structural Risk Minimization principle allows estimating the generalization ability of a learned...
In this work we propose some new generalization bounds for binary classifiers, based on global Radem...
In this work we propose some new generalization bounds for binary classifiers, based on global Radem...
We derive in this paper a new Local Rademacher Complexity risk bound on the generalization ability o...
We derive in this paper a new Local Rademacher Complexity risk bound on the generalization ability o...
We derive here new generalization bounds, based on Rademacher Complexity theory, for model selection...
We derive here new generalization bounds, based on Rademacher Complexity theory, for model selection...
We investigate the behaviour of global and local Rademacher averages. We present new error bounds wh...
We show a Talagrand-type concentration inequality for Multi-Task Learning (MTL), with which we estab...
We derive in this work new upper bounds for estimating the generalization error of kernel classifier...
We derive in this work new upper bounds for estimating the generalization error of kernel classifier...
We derive in this work new upper bounds for estimating the generalization error of kernel classifier...
We propose new bounds on the error of learning algorithms in terms of a data-dependent notion of com...
We propose new bounds on the error of learning algorithms in terms of a data-dependent notion of com...
We propose new bounds on the error of learning algorithms in terms of a data-dependent notion of com...
The Structural Risk Minimization principle allows estimating the generalization ability of a learned...
In this work we propose some new generalization bounds for binary classifiers, based on global Radem...
In this work we propose some new generalization bounds for binary classifiers, based on global Radem...
We derive in this paper a new Local Rademacher Complexity risk bound on the generalization ability o...
We derive in this paper a new Local Rademacher Complexity risk bound on the generalization ability o...
We derive here new generalization bounds, based on Rademacher Complexity theory, for model selection...
We derive here new generalization bounds, based on Rademacher Complexity theory, for model selection...
We investigate the behaviour of global and local Rademacher averages. We present new error bounds wh...
We show a Talagrand-type concentration inequality for Multi-Task Learning (MTL), with which we estab...
We derive in this work new upper bounds for estimating the generalization error of kernel classifier...
We derive in this work new upper bounds for estimating the generalization error of kernel classifier...
We derive in this work new upper bounds for estimating the generalization error of kernel classifier...
We propose new bounds on the error of learning algorithms in terms of a data-dependent notion of com...
We propose new bounds on the error of learning algorithms in terms of a data-dependent notion of com...
We propose new bounds on the error of learning algorithms in terms of a data-dependent notion of com...
The Structural Risk Minimization principle allows estimating the generalization ability of a learned...
In this work we propose some new generalization bounds for binary classifiers, based on global Radem...
In this work we propose some new generalization bounds for binary classifiers, based on global Radem...
DOI
Add to ORKG