Signal-Dependent Time-Frequency Representations for Classification using a Radially Gaussian Kernel and the Alignment Criterion. P. Honeine & C. Richard In 2007 IEEE/SP 14th Workshop on Statistical Signal Processing, pages 735–739, August, 2007. doi abstract bibtex In this paper, we propose a method for tuning time-frequency distributions with radially Gaussian kernel within a classification framework. It is based on a criterion that has recently emerged from the machine learning literature: the kernel-target alignement. Our optimization scheme is very similar to that proposed by Baraniuk and Jones for signal-dependent time-frequency analysis. The relevance of this approach of improving time-frequency classification accuracy is illustrated through examples.
@inproceedings{p._honeine_signal-dependent_2007,
title = {Signal-{Dependent} {Time}-{Frequency} {Representations} for {Classification} using a {Radially} {Gaussian} {Kernel} and the {Alignment} {Criterion}},
isbn = {2373-0803},
doi = {10.1109/SSP.2007.4301356},
abstract = {In this paper, we propose a method for tuning time-frequency distributions with radially Gaussian kernel within a classification framework. It is based on a criterion that has recently emerged from the machine learning literature: the kernel-target alignement. Our optimization scheme is very similar to that proposed by Baraniuk and Jones for signal-dependent time-frequency analysis. The relevance of this approach of improving time-frequency classification accuracy is illustrated through examples.},
booktitle = {2007 {IEEE}/{SP} 14th {Workshop} on {Statistical} {Signal} {Processing}},
author = {{P. Honeine} and {C. Richard}},
month = aug,
year = {2007},
keywords = {Computational efficiency, Interference, Kernel, Machine learning, Pattern recognition, Signal analysis, Signal design, Support vector machine classification, Support vector machines, Time frequency analysis},
pages = {735--739}
}
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