Abstract
The paper addresses the problem of using contextual information by neural nets solving problems of contextual nature. The models of a context-dependent neuron and a multi-layer net are recalled and supplemented by the analysis of context-dependent and hybrid nets’ architecture. The context-dependent nets’ properties are discussed and compared with the properties of traditional nets considering the Vapnik-Chervonenkis dimension, contextual classification and solving tasks of contextual nature. The possibilities of applications to classification and control problems are also outlined.
The work is supported by KBN grant in the years 2002–2005
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Ciskowski, P. (2003). Contextual Modeling Using Context-Dependent Feedforward Neural Nets. In: Blackburn, P., Ghidini, C., Turner, R.M., Giunchiglia, F. (eds) Modeling and Using Context. CONTEXT 2003. Lecture Notes in Computer Science(), vol 2680. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-44958-2_35
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DOI: https://doi.org/10.1007/3-540-44958-2_35
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