We propose an online learning algorithm to tackle the problem of learning under limited computational resources in a teacher-student scenario, over multiple visual cues. For each separate cue, we train an online learning algorithm that sacrifices performance in favor of bounded memory growth and fast update of the solution. We then recover back performance by using multiple cues in the online setting. To this end, we use a two-layers structure. In the first layer, we use a budget online learning algorithm for each single cue. Thus, each classifier provides confidence interpretations for target categories. On top of these classifiers, a linear online learning algorithm is added to learn the combination of these cues. As in standard online learning setups, the learning takes place in rounds. On each round, a new hypothesis is estimated as a function of the previous one.We test our algorithm on two student-teacher experimental scenarios and in both cases results show that the algorithm learns the new concepts in real time and generalizes well. © Springer-Verlag 2010.

An online framework for learning novel concepts over multiple cues / Jie, Luo; Orabona, Francesco; Caputo, Barbara. - STAMPA. - 5994:(2010), pp. 269-280. (Intervento presentato al convegno 9th Asian Conference on Computer Vision, ACCV 2009 tenutosi a Xi'an; China nel 23-27 September 2009) [10.1007/978-3-642-12307-8_25].

An online framework for learning novel concepts over multiple cues

CAPUTO, BARBARA
2010

Abstract

We propose an online learning algorithm to tackle the problem of learning under limited computational resources in a teacher-student scenario, over multiple visual cues. For each separate cue, we train an online learning algorithm that sacrifices performance in favor of bounded memory growth and fast update of the solution. We then recover back performance by using multiple cues in the online setting. To this end, we use a two-layers structure. In the first layer, we use a budget online learning algorithm for each single cue. Thus, each classifier provides confidence interpretations for target categories. On top of these classifiers, a linear online learning algorithm is added to learn the combination of these cues. As in standard online learning setups, the learning takes place in rounds. On each round, a new hypothesis is estimated as a function of the previous one.We test our algorithm on two student-teacher experimental scenarios and in both cases results show that the algorithm learns the new concepts in real time and generalizes well. © Springer-Verlag 2010.
2010
9th Asian Conference on Computer Vision, ACCV 2009
Theoretical Computer Science; Computer Science (all)
04 Pubblicazione in atti di convegno::04b Atto di convegno in volume
An online framework for learning novel concepts over multiple cues / Jie, Luo; Orabona, Francesco; Caputo, Barbara. - STAMPA. - 5994:(2010), pp. 269-280. (Intervento presentato al convegno 9th Asian Conference on Computer Vision, ACCV 2009 tenutosi a Xi'an; China nel 23-27 September 2009) [10.1007/978-3-642-12307-8_25].
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11573/951694
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