Recursive Similarity-Based Algorithm for Deep Learning

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dc.contributor.author Duch, Włodzisław
dc.contributor.author Maszczyk, Tomasz
dc.date.accessioned 2012-12-14T08:52:33Z
dc.date.available 2012-12-14T08:52:33Z
dc.date.issued 2012
dc.identifier.citation Neural Information Processing 19th International Conference, ICONIP 2012, Doha, Qatar, November 12-15, 2012, Proceedings, Part III, pp. 390–397
dc.identifier.isbn 978-3-642-34486-2
dc.identifier.uri http://repozytorium.umk.pl/handle/item/218
dc.description.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.
dc.language.iso eng
dc.publisher Springer
dc.relation.ispartofseries Lecture Notes in Computer Science;7665
dc.rights info:eu-repo/semantics/openAccess
dc.subject similarity-based learning
dc.subject deep networks
dc.subject machine learning
dc.subject k nearest neighbors
dc.title Recursive Similarity-Based Algorithm for Deep Learning
dc.type info:eu-repo/semantics/article

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