@inproceedings{de41af925c054cbb9333ae7b20aa5454,
title = "Bandwidth selection for kernel-based classification",
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.",
author = "Ofir Lindenbaum and Arie Yeredor and Amir Averbuch",
note = "Publisher Copyright: {\textcopyright} 2016 IEEE.; 2016 IEEE International Conference on the Science of Electrical Engineering, ICSEE 2016 ; Conference date: 16-11-2016 Through 18-11-2016",
year = "2017",
month = jan,
day = "4",
doi = "https://doi.org/10.1109/icsee.2016.7806089",
language = "الإنجليزيّة",
series = "2016 IEEE International Conference on the Science of Electrical Engineering, ICSEE 2016",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
booktitle = "2016 IEEE International Conference on the Science of Electrical Engineering, ICSEE 2016",
address = "الولايات المتّحدة",
}