Novelty detection
From Wikipedia, the free encyclopedia
Novelty detection is the identification of new or unknown data or signals that a machine learning system is not aware of during training[1]. Novelty detection is one-class classification[2]. The known data form one class, and a novelty-detection method tries to identify outliers that differ from the distribution of ordinary data, which formed the single data class[1]. Compared to multi-class classification, one-class classification is useful if outliers are sparse compared to ordinary data.
[edit] References
- ^ a b M. Markou, S. Singh, Novelty detection: A review, part 1: Statistical approaches, Signal Processing 83, 2481–2497, 2003
- ^ Tax, D. (2001) One-class classification: Concept-learning in the absence of counter-examples. Doctoral Dissertation, University of Delft, The Netherlands.
 |
This computing article is a stub. You can help Wikipedia by expanding it. |
