Paper Info
Title
Learning Resource-Aware Classifiers for Mobile Devices: From Regularization to Energy Efficiency.
Abstract
Mobile devices are resource-limited systems that provide a large number of services and features. Smartphones, for example, implement advanced functionalities and services for the final user, in addition to conventional communication capabilities. Machine Learning algorithms can help in providing such advanced functionalities, but mobile systems suffer from issues related to their resource-limited nature such as, for example, limited battery capacity and processing power and, therefore, even simple pattern recognition activities can become too demanding, in this respect. We propose here a method to design a Human Activity Recognition algorithm, which takes into account the fact that only limited resources are available for its execution. In particular, we restrict the hypothesis space of possible recognition models by applying some advanced concepts from Statistical Learning Theory, so as to force the selection of models with good generalization ability but low computational complexity. Then, the learned model can be effectively implemented on a mobile and resource-limited device: the experiments, carried out on a current-generation smartphone, show the benefits of the proposed approach in terms of both model accuracy and battery duration.
Year
DOI
Venue
2015
10.1016/j.neucom.2014.12.099
Neurocomputing
Keywords
Field
DocType
Local Rademacher Complexity,Supervised Learning,Energy efficiency,Mobile devices,Performance Assessment
Statistical learning theory,Activity recognition,Computer science,Efficient energy use,Supervised learning,Mobile device,Artificial intelligence,Battery (electricity),Machine learning,restrict,Computational complexity theory
Journal
Volume
ISSN
Citations 
169
0925-2312
4
PageRank 
References 
Authors
0.37
18
4
Name
Order
Citations
PageRank
Luca Oneto183063.22
Alessandro Ghio266735.71
Sandro Ridella3677140.62
Davide Anguita4100170.58