Abstract
Recursive Similarity-Based Learning algorithm (RSBL) follows the deep learning idea, exploiting similarity-based methodology to recursively generate new features. Each transformation layer is generated separately, using as inputs information from all previous layers, and as new features similarity to the k nearest neighbors scaled using Gaussian kernels. In the feature space created in this way results of various types of classifiers, including linear discrimination and distance-based methods, are significantly improved. As an illustrative example a few non-trivial benchmark datasets from the UCI Machine Learning Repository are analyzed.
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Maszczyk, T., Duch, W. (2012). Recursive Similarity-Based Algorithm for Deep Learning. In: Huang, T., Zeng, Z., Li, C., Leung, C.S. (eds) Neural Information Processing. ICONIP 2012. Lecture Notes in Computer Science, vol 7665. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-34487-9_48
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DOI: https://doi.org/10.1007/978-3-642-34487-9_48
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