Locally Optimized Kernels

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dc.contributor.author Duch, Włodzisław
dc.contributor.author Maszczyk, Tomasz
dc.date.accessioned 2012-12-19T16:29:52Z
dc.date.available 2012-12-19T16:29:52Z
dc.date.issued 2012
dc.identifier.citation Artificial Intelligence and Soft Computing: 11th International Conference, ICAISC 2012, Zakopane, Poland, April 29-May 3, 2012, Proceedings, Part I, pp. 412-420
dc.identifier.isbn 978-3-642-29346-7
dc.identifier.uri http://repozytorium.umk.pl/handle/item/274
dc.description.abstract Support Vector Machines (SVM’s) with various kernels have become very successful in pattern classification and regression. However, single kernels do not lead to optimal data models. Replacing the input space by a kernel-based feature space in which the linear discrimination problem with margin maximization is solved is a general method that allows for mixing various kernels and adding new types of features. We show here how to generate locally optimized kernels that facilitate multi-resolution and can handle complex data distributions using simpler models than the standard data formulation may provide.
dc.language.iso eng
dc.publisher Springer
dc.relation.ispartofseries Lecture Notes in Computer Science;7267
dc.rights info:eu-repo/semantics/openAccess
dc.subject artificial intelligence
dc.subject information systems applications
dc.subject computation by abstract devices
dc.subject database management
dc.subject information storage and retrieval
dc.subject computer imaging
dc.subject pattern recognition and graphics
dc.title Locally Optimized Kernels
dc.type info:eu-repo/semantics/bookPart

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