2003 NoveltyDetectionAReviewpart1Sta

• (Markou & Singh, 2003) ⇒ Markos Markou, and Sameer Singh. (2003). “Novelty Detection: A Review — part 1: Statistical Approaches.” In: Signal processing, 83(12).

Subject Headings: Outlier Detection.

Cited By

• http://scholar.google.com/scholar?q=%222003%22+Novelty+Detection%3A+A+Review%E2%80%94part+1%3A+Statistical+Approaches

Quotes

Abstract

Novelty detection is the identification of new or unknown data or signal that a machine learning system is not aware of during training. Novelty detection is one of the fundamental requirements of a good classification or identification system since sometimes the test data contains information about objects that were not known at the time of training the model. In this paper we provide state-of-the-art review in the area of novelty detection based on statistical approaches. The second part of the paper details novelty detection using neural networks. As discussed, there are a multitude of applications where novelty detection is extremely important including signal processing, computer vision, pattern recognition, data mining, and robotics.

References

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	Author	volume	Date Value	title	type	journal	titleUrl	doi	note	year
2003 NoveltyDetectionAReviewpart1Sta	Sameer Singh Markos Markou			Novelty Detection: A Reviewâpart 1: Statistical Approaches						2003