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- NicolÃ² Cesa-Bianchi, Alex Conconi, Claudio Gentile
- IEEE Transactions on Information Theory
- 2001

In this paper, it is shown how to extract a hypothesis with small risk from the ensemble of hypotheses generated by an arbitrary on-line learning algorithm run on an independent and identicallyâ€¦ (More)

- Peter Auer, NicolÃ² Cesa-Bianchi, Claudio Gentile
- J. Comput. Syst. Sci.
- 2000

Most of the performance bounds for on-line learning algorithms are proven assuming a constant learning rate. To optimize these bounds, the learning rate must be tuned based on quantities that areâ€¦ (More)

- Giovanni Cavallanti, NicolÃ² Cesa-Bianchi, Claudio Gentile
- Machine Learning
- 2006

Shifting bounds for on-line classification algorithms ensure good performance on any sequence of examples that is well predicted by a sequence of changing classifiers. When proving shifting boundsâ€¦ (More)

We study the problem of hierarchical classification when labels corresponding to partial and/or multiple paths in the underlying taxonomy are allowed. We introduce a new hierarchical loss function,â€¦ (More)

- NicolÃ² Cesa-Bianchi, Claudio Gentile, Luca Zaniboni
- Journal of Machine Learning Research
- 2006

A selective sampling algorithm is a learning algorithm for classification that, based on the past observed data, decides whether to ask the label of each new instance to be classified. In this paper,â€¦ (More)

- Claudio Gentile
- NIPS
- 2000

A new incremental learning algorithm is described which approximates the maximal margin hyperplane w.r.t. norm p â‰¥ 2 for a set of linearly separable data. Our algorithm, called almap (Approximateâ€¦ (More)

- Claudio Gentile
- Machine Learning
- 1999

We consider two on-line learning frameworks: binary classification through linear threshold functions and linear regression. We study a family of on-line algorithms, called p-norm algorithms,â€¦ (More)

- NicolÃ² Cesa-Bianchi, Alex Conconi, Claudio Gentile
- COLT
- 2002

Kernel-based linear-threshold algorithms, such as support vector machines and Perceptron-like algorithms, are among the best available techniques for solving pattern classification problems. In thisâ€¦ (More)

- Antonio Frangioni, Claudio Gentile
- Math. Program.
- 2006

We show that the convex envelope of the objective function of Mixed-Integer Programming problems with a specific structure is the perspective function of the continuous part of the objectiveâ€¦ (More)

We design and analyze interacting online algorithms for multitask classification that perform better than independent learners whenever the tasks are related in a certain sense. We formalize taskâ€¦ (More)