Abstract
Many different approaches for the modeling of neural networks were presented in the literature (e.g. [4]). Generally the object-oriented approach proved itself as most appropriate. It provides a concise but comprehensive framework for the design of neural networks in terms of its static and dynamic components, i.e. the information structure and its methods in the object-oriented notion.
This paper presents a framework for the conceptual and physical integration of neural networks into object-relational database systems. The static components comprise the structural parts of a neural network, as the neurons and connections, higher topological structures as layers, blocks and network systems. The dynamic components are the behavioral characteristics, as the creation, training and evaluation of the network. Finally the implementation of the new NeuroOracle system based on the proposed framework is presented.
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Schikuta, E., Glantschnig, P. (2007). NeuroOracle: Integration of Neural Networks into an Object-Relational Database System. In: Liu, D., Fei, S., Hou, Z., Zhang, H., Sun, C. (eds) Advances in Neural Networks – ISNN 2007. ISNN 2007. Lecture Notes in Computer Science, vol 4492. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-72393-6_132
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DOI: https://doi.org/10.1007/978-3-540-72393-6_132
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-72392-9
Online ISBN: 978-3-540-72393-6
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