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. © 2012 Springer-Verlag.
CITATION STYLE
Maszczyk, T., & Duch, W. (2012). Recursive similarity-based algorithm for deep learning. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 7665 LNCS, pp. 390–397). https://doi.org/10.1007/978-3-642-34487-9_48
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