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Kernels for time series of exponential decay/growth processes
- Source :
- MLSP, Proc. 22nd IEEE workshop on Machine Learning for Signal Processing (MLSP), Proc. 22nd IEEE workshop on Machine Learning for Signal Processing (MLSP), 2012, Santander, Spain. pp.1-6, ⟨10.1109/MLSP.2012.6349753⟩
- Publication Year :
- 2012
- Publisher :
- IEEE, 2012.
-
Abstract
- International audience; Many processes exhibit exponential behavior. When kernel-based machines are applied on this type of data, conventional kernels such as the Gaussian kernel are not appropriate. In this paper, we derive kernels adapted to time series of exponential decay or growth processes. We provide a theoretical study of these kernels, including the issue of universality. Experimental results are given on a case study: chlorine decay in water distribution systems.
- Subjects :
- exponential behavior
exponential decay processes
02 engineering and technology
one-class
01 natural sciences
support vector machines
[INFO.INFO-TS]Computer Science [cs]/Signal and Image Processing
Gaussian function
exponential distribution
Statistical physics
020701 environmental engineering
Mathematics
chemistry computing
Index Terms-Kernel function
chlorine decay
growth processes
Kernel
normalization
Kernel method
Kernel embedding of distributions
chlorine
Kernel (statistics)
Kernel smoother
symbols
water distribution systems
Chemicals
[SPI.SIGNAL]Engineering Sciences [physics]/Signal and Image processing
Mathematical optimization
Exponential distribution
cybersecurity
0207 environmental engineering
Time series analysis
Gaussian kernel
conventional kernels
kernel methods
symbols.namesake
[INFO.INFO-LG]Computer Science [cs]/Machine Learning [cs.LG]
Machine learning
0103 physical sciences
Training
Exponential decay
010306 general physics
Temperature measurement
kernel-based machines
chemical reactions
Kernel function
[INFO.INFO-CV]Computer Science [cs]/Computer Vision and Pattern Recognition [cs.CV]
one-class classification
Variable kernel density estimation
time series
Subjects
Details
- Database :
- OpenAIRE
- Journal :
- 2012 IEEE International Workshop on Machine Learning for Signal Processing
- Accession number :
- edsair.doi.dedup.....ddd1511cdb5f9b1af4c6173d8660968e
- Full Text :
- https://doi.org/10.1109/mlsp.2012.6349753