@inproceedings{3461b3ce9a6848eebdaef64bfd75eb77,
title = "Parallelizing the Large-Width learning algorithm",
abstract = "We introduce a new parallel algorithm that implements the Large-Width (LW) learning algorithm [3]. The LW algorithm is an instance-based learning procedure which produces a multi-category classifier defined on any distance space, with the property that the classifier has a large sample width (which is similar to the notion of large margin learning). Being instance-based, the LW algorithm spends a majority of the time computing pairwise distances between examples (instances). The parallel version introduced here takes advantage of this fact and processes these computations in parallel. We present pseudo-code and estimate the speedup factor relative to the sequential LW algorithm.",
keywords = "Machine learning, big data, classification, parallel algorithm",
author = "Joel Ratsaby and Alon Sabaty",
note = "Publisher Copyright: {\textcopyright} 2018 IEEE.; 2018 IEEE International Conference on the Science of Electrical Engineering in Israel, ICSEE 2018 ; Conference date: 12-12-2018 Through 14-12-2018",
year = "2018",
month = jul,
day = "2",
doi = "10.1109/ICSEE.2018.8646024",
language = "אנגלית",
series = "2018 IEEE International Conference on the Science of Electrical Engineering in Israel, ICSEE 2018",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
booktitle = "2018 IEEE International Conference on the Science of Electrical Engineering in Israel, ICSEE 2018",
address = "ארצות הברית",
}