Data augmentation: An alternative approach to the analysis of spectroscopic data

Chemometrics and Intelligent Laboratory Systems

Volumn 44, Issue 1-2, 1998, Pages 161-173

  Conlin, A K ^a   Martin, E B ^a   Morris, A J ^a  

^a NEWCASTLE UNIVERSITY   (United Kingdom)

Author keywords

Data augmentation; Gaussian noise; Partial least squares

Indexed keywords

CHEMICAL INDUSTRY; CHEMISTRY; CONFERENCE PAPER; DATA ANALYSIS; MATHEMATICAL ANALYSIS; PRIORITY JOURNAL; SPECTROSCOPY; STATISTICAL ANALYSIS;

EID: 0032517827     PISSN: 01697439     EISSN: None     Source Type: Journal    
DOI: 10.1016/S0169-7439(98)00071-9     Document Type: Conference Paper

References (8)

1. Breiman L. Bagging predictors. Machine Learning. 24(2):1996;123-140.
2. Breiman L. Stacked regressions. Machine Learning. 24(1):1996;49-64.
3. Geman S., Bienenstock E., Doursat R. Neural networks and the bias/variance dilemma. Neural Computation. 4:1992;1-58.
4. Raviv Y., Intrator N. Bootstrapping with noise: an effective regularization technique. Connection Science. 8(3-4):1996;355-372.
5. Wolpert D.H. Stacked generalisation. Neural Networks. 5:1992;241-259.
6. A.K. Conlin, Complex sensor data analysis through data augmentation, PhD Thesis, Newcastle University, 1996.
7. A.K. Conlin, E.B. Martin, A.J. Morris, Minimal data sets and process calibration, Proceedings of the 5th IFAC Symposium on Dynamics and Control of Process Systems, DYCOPS-5, 8-10 June 1998, Corfu, Greece, pp. 365-370.
8. B. Efron, R. Tibshirani, An introduction to the bootstrap, Chapman & Hall, New York, 1993.