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@INPROCEEDINGS{Tommasi_BMVC_2012,
         author = {Tommasi, Tatiana and Orabona, Francesco and Kaboli, Mohsen and Caputo, Barbara},
       projects = {Idiap, EMMA},
          title = {Leveraging over prior knowledge for online learning of visual categories},
      booktitle = {Proceedings of the British Machine Vision Conference},
           year = {2012},
       abstract = {Open ended learning is a dynamic process based on the continuous analysis of new
data, guided by past experience. On one side it is helpful to take advantage of prior
knowledge when only few information on a new task is available (transfer learning). On
the other, it is important to continuously update an existing model so to exploit the new
incoming data, especially if their informative content is very different from what is already
known (online learning). Until today these two aspects of the learning process
have been tackled separately. In this paper we propose an algorithm that takes the best of
both worlds: we consider a sequential learning setting, and we exploit the potentiality of
knowledge transfer with a computationally cheap solution. At the same time, by relying
on past experience we boost online learning to predict reliably on future problems. A
theoretical analysis, coupled with extensive experiments, show that our approach performs
well in terms of the online number of training mistakes, as well as in terms of
performance on separate test sets.},
            pdf = {https://publications.idiap.ch/attachments/papers/2012/Tommasi_BMVC_2012.pdf}
}