Automatic identification of impairments using support vector machine pattern classification on eye diagrams

IEEE Photonics Technology Letters

Volumn 18, Issue 22, 2006, Pages 2398-2400

  Skoog, Ronald A ^a   Banwell, Thomas C ^a   Gannett, Joel W ^a   Habiby, Sarry F ^a   Pang, Marcus ^a   Rauch, Michael E ^a   Toliver, Paul ^a  

^a TELCORDIA TECHNOLOGIES   (United States)

Author keywords

Machine learning; Optical performance monitoring (OPM); Pattern recognition

Indexed keywords

LEARNING SYSTEMS; OPHTHALMOLOGY; PATTERN RECOGNITION;

EYE DIAGRAMS; OPTICAL IMPAIRMENTS; OPTICAL PERFORMANCE MONITORING; SUPPORT VECTOR MACHINE;

OPTICAL DATA PROCESSING;

EID: 37849189893     PISSN: 10411135     EISSN: None     Source Type: Journal    
DOI: 10.1109/LPT.2006.886146     Document Type: Article

References (6)

1. R. Duda, P. Hart, and D. Stork, Pattern Classification. New York: Wiley, 2001.
2. V. Vapnick, The Nature of Statistical Learning Theory. New York: Wiley, 1995.
3. M. K. Hu, "Visual pattern recognition by moment invariants," IRE Trans. Info. Theory, vol. 8, no. 1, pp. 179-187, 1962.
4. M. R. Teague, "Image analysis via the general theory of moments," J. Opt. Soc. Amer., vol. 70, no. 8, pp. 920-930, 1980.
5. R. Mukundan, Moment Functions in Image Analysis. Singapore: World Scientific, 1998.
6. T.-F.Wu, C.-J. Lin, and R. Weng, "Probability estimates for multi-class classification by pairwise coupling," J. Machine Learning Res., vol. 5, pp. 975-1005, 2004.