Contributions to Proceedings:

N. TaheriNejad, A. Jantsch:
"Improved machine learning using confidence";
in: "IEEE Canadian Conference of Electrical and Computer Engineering (CCECE)", IEEE, Edmonton, Canada, 2019, ISBN: 978-1-7281-0319-8, 5 pages.

English abstract:
Wearable gadgets are in for an exponential rise thanks to the improvements in the silicon scaling and ubiquity of Internet as well as battery technology and sensor amelioration. However, despite these advances, wearable gadgets remain resource constrained devices requiring further improvements in all those areas. Self-awareness enables a system to adjust its behaviors to enhance the operations of the system and meet its goals. In this paper, we review one of the self-awareness techniques used in wearable devices and machine learning, namely confidence, which leads to their improvements. In particular, we focus on how confidence helps to maintain or enhance performance of machine learning techniques while reducing the complexity of the processes and required resources for running them on resource constrained devices. We look into three examples, epilepsy monitoring, iris flower detection, and image classification.

"Official" electronic version of the publication (accessed through its Digital Object Identifier - DOI)

Created from the Publication Database of the Vienna University of Technology.