Neural methods for non-standard data

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Neural methods for non-standard data
Standard pattern recognition provides effective and noise-tolerant tools for machine learning tasks; however, most approaches only deal with real vectors of a finite and fixed dimensionality. In this tutorial paper, we give an overview about extensions of pattern recognition towards non-standard data which are not contained in a finite dimensional space, such as strings, sequences, trees, graphs, or functions. Two major directions can be distinguished in the neural networks literature: models can be based on a similarity measure adapted to non-standard data, including kernel methods for structures as a very prominent approach, but also alternative metric based algorithms and functional networks; alternatively, non-standard data can be processed recursively within supervised and unsupervised recurrent and recursive networks and fully recurrent systems.
Barbara Hammer, Brijnesh J. Jain
Added 30 Oct 2010
Updated 30 Oct 2010
Type Conference
Year 2004
Authors Barbara Hammer, Brijnesh J. Jain
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