Statistical Signal Processing for Novelty Detection

@inproceedings{Balan2005StatisticalSP,
  title={Statistical Signal Processing for Novelty Detection},
  author={Radu Balan and Justinian P. Rosca and Paul Bogdan},
  year={2005}
}
The goal of this article is to investigate and suggest techniques for health condition monitoring and diagnosis using machine learning from sensor data. In particular, this arti cle overview and discusses support vector machines methods such as hard margin and soft margin problems. In order to investigate the abnormalities and classify a large se t of data an iterative Support Vector Machine algorithm was constructed. However, similar techniques could be applied to analyze or monitor for… CONTINUE READING
Highly Influential
This paper has highly influenced 19 other papers. REVIEW HIGHLY INFLUENTIAL CITATIONS
Highly Cited
This paper has 210 citations. REVIEW CITATIONS

From This Paper

Figures, tables, results, connections, and topics extracted from this paper.
132 Extracted Citations
3 Extracted References
Similar Papers

Citing Papers

Publications influenced by this paper.
Showing 1-10 of 132 extracted citations

210 Citations

01020'05'08'11'14'17
Citations per Year
Semantic Scholar estimates that this publication has 210 citations based on the available data.

See our FAQ for additional information.

Referenced Papers

Publications referenced by this paper.
Showing 1-3 of 3 references

Bennet - A Linear Programming Approach to Novelty Detection

  • Colin Cambell, P Kristin
  • Neural Information Processing Systems,
  • 2000
1 Excerpt

Similar Papers

Loading similar papers…