Bandwidth selection for kernel-based classification

Ofir Lindenbaum, Arie Yeredor, Amir Averbuch

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

Dimensionality reduction is an essential step in various machine learning tasks. Applying classification algorithms to the reduced space is often more efficient and accurate. We focus on kernel based dimensionality reduction techniques, and propose to set the bandwidth such that a coherent mapping is extracted. The proposed framework is simulated on artificial and real dataset, results show a high correlation between optimal classification rates and the proposed bandwidth.

Original languageEnglish
Title of host publication2016 IEEE International Conference on the Science of Electrical Engineering, ICSEE 2016
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781509021529
DOIs
StatePublished - 4 Jan 2017
Event2016 IEEE International Conference on the Science of Electrical Engineering, ICSEE 2016 - Eilat, Israel
Duration: 16 Nov 201618 Nov 2016

Publication series

Name2016 IEEE International Conference on the Science of Electrical Engineering, ICSEE 2016

Conference

Conference2016 IEEE International Conference on the Science of Electrical Engineering, ICSEE 2016
Country/TerritoryIsrael
CityEilat
Period16/11/1618/11/16

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