TY - GEN
T1 - Bandwidth selection for kernel-based classification
AU - Lindenbaum, Ofir
AU - Yeredor, Arie
AU - Averbuch, Amir
N1 - Publisher Copyright:
© 2016 IEEE.
PY - 2017/1/4
Y1 - 2017/1/4
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=85014159280&partnerID=8YFLogxK
U2 - 10.1109/ICSEE.2016.7806089
DO - 10.1109/ICSEE.2016.7806089
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AN - SCOPUS:85014159280
T3 - 2016 IEEE International Conference on the Science of Electrical Engineering, ICSEE 2016
BT - 2016 IEEE International Conference on the Science of Electrical Engineering, ICSEE 2016
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2016 IEEE International Conference on the Science of Electrical Engineering, ICSEE 2016
Y2 - 16 November 2016 through 18 November 2016
ER -