Abstract
This work proposes an agglomerative hierarchical clustering algorithm where the items to be clustered are supervised-learning classifiers. The measure of similarity to compare classifiers is based on their behaviour. This clustering algorithm has been applied to document enhancement: A set of neural filters is trained with multilayer perceptrons for different types of noise and then clustered into groups to obtain a reduced set of neural clustered filters. In order to automatically determine which clustered filter is the most suitable to clean and enhance a real noisy image, an image classifier is also trained using multilayer perceptrons.
This work has been partially supported by the Spanish Government under contract TIN2006-12767, by the Generalitat Valenciana under contract GVA06/302, and the Universidad Politécnica de Valencia under contract 20070448.
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Zamora-Martínez, F., España-Boquera, S., Castro-Bleda, M.J. (2007). Behaviour-Based Clustering of Neural Networks Applied to Document Enhancement. In: Sandoval, F., Prieto, A., Cabestany, J., Graña, M. (eds) Computational and Ambient Intelligence. IWANN 2007. Lecture Notes in Computer Science, vol 4507. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-73007-1_18
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DOI: https://doi.org/10.1007/978-3-540-73007-1_18
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