Dear guest, welcome to this publication database. As an anonymous user, you will probably not have edit rights. Also, the collapse status of the topic tree will not be persistent. If you like to have these and other options enabled, you might ask Admin (Ivan Eggel) for a login account.
 [BibTeX] [RIS]
Towards Retraining of Machine Learning Algorithms: An Efficiency Analysis Applied to Smart Agriculture
Type of publication: Inproceedings
Citation:
Booktitle: 2020 Global Internet of Things Summit (GIoTS)
Year: 2020
Month: June
Pages: 1-6
Publisher: IEEE
Location: Dublin, Ireland
ISBN: 978-1-7281-6728-2
DOI: 10.1109/GIOTS49054.2020.9119601
Abstract: This paper compares the efficiency of state-of-the-art machine learning algorithms used to detect an object in an image. A comparison between a deep learning algorithm such as the VGG-16 and a well-tuned random forest algorithm using classical image analysis parameters is presented. To estimate the efficiency, the classification performances like AUC, precision, recall and computation time of the algorithm retraining process are used. The experimental set-up shows that a well-tuned random forest algorithm is equal to, or better than, the deep learning approach and increases the speed of the retraining process by a factor of around 400.
Keywords: Active Learning, aerial images, decision tree ensemble, Image recognition, machine learning, Neural Network, Precision agriculture, Prediction, Random Forests, Real-Tme Retraining, Smart Agriculture
Authors Treboux, Jerome
Ingold, Rolf
Genoud, Dominique
Added by: []
Total mark: 0
Attachments
  • Towards Retraining of Machine ...
Notes
    Topics