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Communication Dans Un Congrès Année : 2016

Incremental learning algorithms and applications

Résumé

Incremental learning refers to learning from streaming data, which arrive over time, with limited memory resources and, ideally, without sacrificing model accuracy. This setting fits different application scenarios where lifelong learning is relevant, e.g. due to changing environments , and it offers an elegant scheme for big data processing by means of its sequential treatment. In this contribution, we formalise the concept of incremental learning, we discuss particular challenges which arise in this setting, and we give an overview about popular approaches, its theoretical foundations, and applications which emerged in the last years.
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Dates et versions

hal-01418129 , version 1 (16-12-2016)

Identifiants

  • HAL Id : hal-01418129 , version 1

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Alexander Gepperth, Barbara Hammer. Incremental learning algorithms and applications. European Symposium on Artificial Neural Networks (ESANN), 2016, Bruges, Belgium. ⟨hal-01418129⟩
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