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Apprentissage de dictionnaires d’ondelettes vaste marge pour la classification de signaux et de textures

Florian Yger, Alain Rakotomamonjy, Revue d’intelligence artificielle 2011, 25 (3)

doi:10.3166/ria.25.369-392

 

Argyriou A., Hauser R., Micchelli C., Pontil M., A DC-Programming Algorithm for Kernel Selection, ICML, p. 41-48, 2006.

Bach F., Exploring Large Feature Spaces with Hierarchical Multiple Kernel Learning,NIPS, p. 105-112, 2008.

Bach F., Lanckriet G., Jordan M., Multiple kernel learning, conic duality, and the SMO algorithm, ICML, p. 6-13, 2004.

Bonnans J., Shapiro A., Optimization problems with pertubation : A guided tour, SIAM Review, vol. 40, n° 2, p. 202-227, 1998.

Buckheit J., Donoho D., Improved linear discrimination using time-frequency dictionaries, Society of Photo-Optical Instrumentation Engineers (SPIE) Conference Series, p. 540-551, 1995a.

Buckheit J., Donoho D., Wavelab and reproducible research, in Springerr-Verlag (ed.), Wavelets and Statistics, p. 55-81, 1995b.

Busch A., Boles W., Texture classification using multiple wavelet analysis, Proceedings of the Sixth Digital Image Computing: Techniques and Applications conference, p. 341-345, 2002.

Cabrera A., Dremstrup K., Auditory and spatial navigation imagery in Brain-Computer Interface using optimized wavelets, Journal of Neuroscience Methods, vol. 174, n° 1, p. 135-146, 2008.

Chapelle O., Rakotomamonjy A., Second order optimization of kernel parameters, NIPS Workshop on Automatic Selection of Optimal Kernels, p. 4, 2008.

Farina D., do Nascimento O. F., Lucas M.-F., Doncarli C., Optimization of wavelets for classification of movement-related cortical potentials generated by variation of force-related parameters, Journal of Neuroscience Methods, vol. 162, n° 1-2, p. 357 - 363, 2007.

Gehler P., Nowozin S., Infinite Kernel Learning, NIPS workshop on Automatic Selection of Kernel Parameters, p. 12, 2008.

Guler I., Ubeyli E., ECG beat classifier designed by combined neural network model, Pattern Recognition, vol. 38, n° 2, p. 199-208, 2005.

Huang K., Aviyente S., Sparse representation for signal classification, NIPS, p. 609-616, 2006.

Ince N., Tewfik A., Arica S., Extraction subject-specific motor imagery time-frequency patterns for single trial EEG classification, Computers in Biology and Medicine, vol. 37, n° 4, p. 499-508, 2007.

Kara S., Okandan M., Atrial fibrillation classification with artificial neural networks,Pattern Recognition, vol. 40, n° 11, p. 2967-2973, 2007.

Kim S. C., Kang T. J., Texture classification and segmentation using wavelet packet frame and Gaussian mixture model, Pattern Recognition, vol. 40, n° 4, p. 1207-1221, 2007.

Kloft M., Brefeld U., Sonnenburg S., Laskov P., Müller K.-R., Zien A., Efficient and Accurate Lp-norm Multiple Kernel Learning, NIPS, p. 997-1005, 2009.

Lanckriet G., Cristianini N., El Ghaoui L., Bartlett P., Jordan M., Learning the Kernel Matrix with Semi-Definite Programming, Journal of Machine Learning Research, vol. 5, p. 27-72, 2004.

Li S., Kwok J., Zhu H., Wang Y., Texture classification using support vector machines, Pattern Recognition, vol. 36, n° 12, p. 2883-2893, 2003.

Lucas M.-F., Gaufriau A., Pascual S., Doncarli C., Farina D., Multi-channel surface EMG classification using support vector machines and signal-based wavelet optimization, Biomedical Signal Processing and Control, vol. 3, n° 2, p. 169 - 174, 2008.

Mairal J., Bach F., Ponce J., Sapiro G., Zisserman A., Supervised Dictionary Learning, NIPS, 2008.

Mallat S., A wavelet tour of signal processing, Academic Press, 1998.

Neumann J., Schnorr C., Steidl G., Efficient wavelet adaptation for hybrid wavelet-large margin classifiers, Pattern Recognition, vol. 38, n° 11, p. 1815-1830, 2005.

Nocedal J., Wright S., Numerical optimization, Springer, 2000.

Rakotomamonjy A., Bach F., Grandvalet Y., Canu S., SimpleMKL, Journal of Machine Learning Research, vol. 9, p. 2491-2521, 2008a.

Rakotomamonjy A., Guigue V., BCI Competition III: Dataset II - Ensemble of SVMs for BCI P300 speller, IEEE Trans. Biomedical Engineering, vol. 55, n° 3, p. 1147-1154, 2008b.

Saito N., Coifman R., Local Discriminant bases and their applications, Journal of Math. Vision and Imaging, vol. 5, n° 4, p. 337-358, 1995.

Sherlock B. G., Monro D. M., On the space of orthonormal wavelets, IEEE Transactions on Signal Processing, vol. 46, n° 6, p. 1716-1720, 1998.

Shevade S., Keerthi S., A simple and efficient algorithm for gene selection using sparse logistic regression, Bioinformatics, vol. 19, n° 17, p. 2246-2253, 2003.

Strauss D., Steidl G., Hybrid wavelet-support vector classification of waveforms, Journal of computational and applied mathematics, vol. 148, n° 2, p. 375-400, 2002.

Suzuki T., Tomioka R., SpicyMKL, Technical Report n° 0909.5026, arXiv, 2009.

Szafranski M., Grandvalet Y., Rakotomamonjy A., Composite Kernel Learning, ICML, p. 1040-1047, 2008.

Vishwanathan S. V. N., Smola A. J., Murty M., SimpleSVM, ICML, p. 760-767, 2003.

Xu Z., Jin R., King I., Lyu M., An Extended Level Method for Multiple Kernel Learning, NIPS, p. 1825-1832, 2008.

Xu Z., Jin R., Yang H., King I., Lyu M., Simple and efficient multiple kernel learning by group lasso, ICML, p. 1175-1182, 2010.

Zandi A., Moradi M., Quantitative evaluation of a wavelet-based method in ventricular late potential detection, Pattern Recognition, vol. 39, n° 7, p. 1369-1379, 2006.