Abstract
In the last chapter, supervised learning has already been used to classify the outputs of a Neural Abstraction Pyramid that was trained with unsupervised learning. In this chapter, it is discussed how supervised learning techniques can be applied in the Neural Abstraction Pyramid itself.
After an introduction, supervised learning in feed-forward neural networks is covered. Attention is paid to the issues of weight sharing and the handling of network borders, which are relevant for the Neural Abstraction Pyramid architecture. Section 6.3 discusses supervised learning for recurrent networks. The difficulty of gradient computation in recurrent networks makes it necessary to employ algorithms that use only the sign of the gradient to update the weights.
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© 2003 Springer-Verlag Berlin Heidelberg
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Behnke, S. (2003). Supervised Learning. In: Hierarchical Neural Networks for Image Interpretation. Lecture Notes in Computer Science, vol 2766. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-45169-3_6
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DOI: https://doi.org/10.1007/978-3-540-45169-3_6
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-40722-5
Online ISBN: 978-3-540-45169-3
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