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- Publisher
- Springer Journals
- Copyright
- Copyright © 2012 by Springer Science+Business Media Dordrecht
- Subject
- Computer Science; Artificial Intelligence (incl. Robotics); Computer Science, general
- ISSN
- 0269-2821
- eISSN
- 1573-7462
- DOI
- 10.1007/s10462-012-9366-7
- Publisher site
- See Article on Publisher Site
Abstract
This paper focuses on machine learning techniques for real-time detection. Although many supervised learning techniques have been described in the literature, no technique always performs best. Several comparative studies are available, but have not always been performed carefully, leading to invalid conclusions. Since benchmarking all techniques is a tremendous task, literature has been used to limit the available options, selecting the two most promising techniques (AdaBoost and SVM), out of 11 different Machine Learning techniques. Based on a thorough comparison using 2 datasets and simulating noise in the feature set as well as in the labeling, AdaBoost is concluded to be the best machine learning technique for real-time target detection as its performance is comparable to SVM, its detection time is one or multiple orders of magnitude faster, its inherent feature selection eliminates this as a separate task, while it is more straightforward to use (only three coupled parameters to tune) and has a lower training time.
Journal
Artificial Intelligence Review – Springer Journals
Published: Nov 6, 2012