Person

Barbara Caputo

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Related publications (74)

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Scalable greedy algorithms for transfer learning

Barbara Caputo, Ilja Kuzborskij

In this paper we consider the binary transfer learning problem, focusing on how to select and combine sources from a large pool to yield a good performance on a target task. Constraining our scenario to real world, we do not assume the direct access to the ...

2017

Transfer Learning through Greedy Subset Selection

Barbara Caputo, Ilja Kuzborskij

In this work we study the binary transfer learning problem involving 10^2 -10^3 sources. We focus on how to select sources from the large pool and how to combine them to yield a good performance on a target task. In particular, we consider the transfer lea ...

Idiap2015

Transfer Learning Through Greedy Subset Selection

Barbara Caputo, Ilja Kuzborskij

We study the binary transfer learning problem, focusing on how to select sources from a large pool and how to combine them to yield a good performance on a target task. In particular, we consider the transfer learning setting where one does not have direct ...

Springer Int Publishing Ag2015

Characterization of a Benchmark Database for Myoelectric Movement Classification

Barbara Caputo, Ilja Kuzborskij

In this paper, we characterize the Ninapro database and its use as a benchmark for hand prosthesis evaluation. The database is a publicly available resource that aims to support research on advanced myoelectric hand prostheses. The database is obtained by ...

2015

A Deeper Look at Dataset Bias

Barbara Caputo, Tatiana Tommasi, Novi Patricia

The presence of a bias in each image data collection has recently attracted a lot of attention in the computer vision community showing the limits in generalization of any learning method trained on a specific dataset. At the same time, with the rapid deve ...

Springer Int Publishing Ag2015

Scene Recognition with Naive Bayes Non-linear Learning

Barbara Caputo, Marco Fornoni

A crucial feature of a good scene recognition algorithm is its ability to generalize. Scene categories, especially those related to human made indoor places or to human activities like sports, do present a high degree of intra-class variability, which in t ...

2014

Stable Myoelectric Control of a Hand Prosthesis using Non-Linear Incremental Learning

Barbara Caputo

Stable myoelectric control of hand prostheses remains an open problem. The only successful human–machine interface is surface electromyography, typically allowing control of a few degrees of freedom. Machine learning techniques may have the potential to re ...

2014

The Movement Error Rate for Evaluation of Machine Learning Methods for sEMG-based Hand Movement Classification

Barbara Caputo

There has been increasing interest in applying learning algorithms to improve the dexterity of myoelectric prostheses. In this work, we present a large-scale benchmark evaluation on the second iteration of the publicly released NinaPro database, which cont ...

2014

Learning Categories from Few Examples with Multi Model Knowledge Transfer

Barbara Caputo, Tatiana Tommasi

Learning a visual object category from few samples is a compelling and challenging problem. In several real-world applications collecting many annotated data is costly and not always possible. However a small training set does not allow to cover the high i ...

Idiap2013

Improving Control of Dexterous Hand Prostheses Using Adaptive Learning

Barbara Caputo, Tatiana Tommasi

At the time of this writing, the main means of control for polyarticulated self-powered hand prostheses is surface electromyography (sEMG). In the clinical setting, data collected from two electrodes are used to guide the hand movements selecting among a f ...

Ieee-Inst Electrical Electronics Engineers Inc2013