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Towards a Better Understanding of Incremental Learning

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Algorithmic Learning Theory (ALT 2006)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 4264))

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Abstract

The present study aims at insights into the nature of incremental learning in the context of Gold’s model of identification in the limit. With a focus on natural requirements such as consistency and conservativeness, incremental learning is analysed both for learning from positive examples and for learning from positive and negative examples. The results obtained illustrate in which way different consistency and conservativeness demands can affect the capabilities of incremental learners. These results may serve as a first step towards characterising the structure of typical classes learnable incrementally and thus towards elaborating uniform incremental learning methods.

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Author information

Authors and Affiliations

  1. School of Computing, National University of Singapore, 117543, Singapore

    Sanjay Jain

  2. FB Informatik, Hochschule Darmstadt, Haardtring 100, D–64295, Darmstadt

    Steffen Lange

  3. DFKI GmbH, Erwin-Schrödinger-Str. 57, D–67663, Kaiserslautern

    Sandra Zilles

Authors
  1. Sanjay Jain
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  2. Steffen Lange
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  3. Sandra Zilles
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Editor information

Editors and Affiliations

  1. Departament de Llenguatges i Sistemes Informàtics Laboratori d’Algorísmica Relacional, Complexitat i Aprenentatge, Universitat Politècnica de Catalunya, Barcelona,  

    José L. Balcázar

  2. Google, 1600 Amphitheatre Parkway, 94043, Mountain View, CA, USA

    Philip M. Long

  3. Department of Computer Science and Department of Mathematics, National University of Singapore, 117543, Singapore, Republic of Singapore

    Frank Stephan

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© 2006 Springer-Verlag Berlin Heidelberg

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Jain, S., Lange, S., Zilles, S. (2006). Towards a Better Understanding of Incremental Learning. In: Balcázar, J.L., Long, P.M., Stephan, F. (eds) Algorithmic Learning Theory. ALT 2006. Lecture Notes in Computer Science(), vol 4264. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11894841_16

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  • DOI: https://doi.org/10.1007/11894841_16

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-46649-9

  • Online ISBN: 978-3-540-46650-5

  • eBook Packages: Computer ScienceComputer Science (R0)

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