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- W4211036582 abstract "Free Access References José Luis Rojo-Álvarez, José Luis Rojo-Álvarez Department of Signal Theory and Communications, University Rey Juan Carlos, Fuenlabrada (Madrid) Center for Computational Simulation, Universidad Politécnica de Madrid, SpainSearch for more papers by this authorManel Martínez-Ramón, Manel Martínez-Ramón Department of Electrical and Computer Engineering, The University of New Mexico, Albuquerque, New Mexico, USASearch for more papers by this authorJordi Muñoz-Marí, Jordi Muñoz-Marí Department of Electronics Engineering, Universitat de València, Paterna (València), SpainSearch for more papers by this authorGustau Camps-Valls, Gustau Camps-Valls Department of Electronics Engineering, Universitat de València, Paterna (València), SpainSearch for more papers by this author Book Author(s):José Luis Rojo-Álvarez, José Luis Rojo-Álvarez Department of Signal Theory and Communications, University Rey Juan Carlos, Fuenlabrada (Madrid) Center for Computational Simulation, Universidad Politécnica de Madrid, SpainSearch for more papers by this authorManel Martínez-Ramón, Manel Martínez-Ramón Department of Electrical and Computer Engineering, The University of New Mexico, Albuquerque, New Mexico, USASearch for more papers by this authorJordi Muñoz-Marí, Jordi Muñoz-Marí Department of Electronics Engineering, Universitat de València, Paterna (València), SpainSearch for more papers by this authorGustau Camps-Valls, Gustau Camps-Valls Department of Electronics Engineering, Universitat de València, Paterna (València), SpainSearch for more papers by this author First published: 05 January 2018 https://doi.org/10.1002/9781118705810.refs AboutPDFPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShareShare a linkShare onFacebookTwitterLinked InRedditWechat References Abbate, A., Koay, J., Frankel, J., Schroeder, S., and Das, P. (1997). Signal detection and noise suppression using a wavelet transform signal processor: application to ultrasonic flaw detection. IEEE Transactions on Ultrasonics, Ferroelectrics, and Frequency Control, 44(1), 14– 26. Abe, N. and Mamitsuka, H. (1998). Query learning strategies using boosting and bagging. In J. Shavlik, editor, ICML '98 Proceedings of the Fifteenth International Conference on Machine Learning, pages 1– 9. Morgan Kaufmann, San Francisco, CA. Abrahamsen, T. J. and Hansen, L. K. (2011). A cure for variance inflation in high dimensional kernel principal component analysis. Journal of Machine Learning Research, 12, 2027– 2044. Adali, T. and Liu, X. (1997). Canonical piecewise linear network for nonlinear filtering and its application to blind equalization. Signal Processing, 61(2), 145– 155. Aharon, M., Elad, M., and Bruckstein, A. (2006). K-SVD: an algorithm for designing overcomplete dictionaries for sparse representation. IEEE Transactions on Signal Processing, 54(11), 4311– 4322. Ahmed, T., Oreshkin, B., and Coates, M. (2007). Machine learning approaches to network anomaly detection. In J. Chase and I. Cohen, editors, SYSML'07 Proceedings of the 2nd USENIX Workshop on Tackling Computer Systems Problems with Machine Learning Techniques, SYSML'07, pages 7:1– 7:6. USENIX Association, Berkeley, CA. Aizerman, M. A., Braverman, E. M., and Rozoner, L. (1964). Theoretical foundations of the potential function method in pattern recognition learning. Automation and Remote Control, 25, 821– 837. Akaike, H. (1974). A new look at the statistical model identification. IEEE Transactions on Automatic Control, 19(6), 716– 723. Allwein, E. L., Schapire, R. E., and Singer, Y. (2001). Reducing multiclass to binary: a unifying approach for margin classifiers. Journal of Machine Learning Research, 1, 113– 141. Alper, P. (1965). A consideration of the discrete volterra series. IEEE Transactions on Automatic Control, 10(3), 322– 327. Altun, Y., Tsochantaridis, I., and Hofmann, T. (2003). Hidden Markov support vector machines. In T. Fawcett and N. Mishra, editors, Proceedings of the Twentieth International Conference on Machine Learning (ICML-2003), Washington, DC, pages 3– 10. AAAI Press, Menlo Park, CA. Altun, Y., Hofmann, T., and Tsochantaridis, I. (2007). Support vector learning for interdependent and structured output spaces. In G. Bakır, T. Hofmann, B. Schölkopf, A. J. Smola, and S. Vishwanathan, editors, Predicting Structured Data, pages 85– 105. MIT Press. Anderson, J. R. (1983). The Architecture of Cognition. Harvard University Press, Cambridge, MA, USA. Andrieu, C., de Freitas, N., Doucet, A., and Jordan, M. (2003). An introduction to MCMC for machine learning. Machine Learning, 50, 5– 43. Anguita, D. and Gagliolo, M. (2002). MDL based model selection for relevance vector regression. In J. Dorronsoro, editor, Artificial Neural Networks – ICANN 2002, pages 468– 473. Springer, Berlin. Arenas-García, J. and Camps-Valls, G. (2008). Efficient kernel orthonormalized PLS for remote sensing applications. IEEE Transactions on Geoscience and Remote Sensing, 46, 2872– 2881. Arenas-García, J. and Figueiras-Vidal, A. R. (2009). Adaptive combination of proportionate filters for sparse echo cancellation. IEEE Transactions on Audio, Speech & Language Processing, 17(6), 1087– 1098. Arenas-García, J. and Petersen, K. B. (2009). Kernel multivariate analsis in remote sensing feature extraction. In G. Camps-Valls and L. Bruzzone, editors, Kernel Methods for Remote Sensing Data Analysis. John Wiley & Sons. Arenas-García, J., Figueiras-Vidal, A. R., and Sayed, A. H. (2006). Mean-square performance of a convex combination of two adaptive filters. IEEE Transactions on Signal Processing, 54(3), 1078– 1090. Arenas-García, J., Petersen, K. B., and Hansen, L. K. (2007). Sparse kernel orthonormalized PLS for feature extraction in large data sets. In B. Schölkopf, J. C. Platt, and T. Hoffman, editors, NIPS'06 Proceedings of the 19th International Conference on Neural Information Processing Systems. MIT Press, Cambridge, MA. Arenas-García, J., Petersen, K. B., Camps-Valls, G., and Hansen, L. K. (2013). Kernel multivariate analysis framework for supervised subspace learning. Signal Processing Magazine, 30(4), 16– 29. Aronszajn, N. (1950). Theory of reproducing kernels. Transactions of the American Mathematical Society, 68(3), 337– 404. Arulampalam, M. S., Maskell, S., Gordon, N., and Clapp, T. (2002). A tutorial on particle filters for online nonlinear/non-Gaussian Bayesian tracking. IEEE Transactions on Signal Processing, 50(2), 174– 188. Aschbacher, E. and Rupp, M. (2005). Robustness analysis of a gradient identification method for a nonlinear Wiener system. In Proceedings of the 2005 IEEE/SP 13th Workshop on Statistical Signal Processing (SSP 2005), Bordeaux, France. Atkinson, K. (1978). An Introduction to Numerical Analysis. John Wiley & Sons. Aurenhammer, F. (1991). Voronoi diagrams – a survey of a fundamental geometric data structure. ACM Computing Surveys, 23(3), 345– 405. Babaie-Zadeh, M., Jutten, C., and Nayebi, K. (2002). A geometric approach for separating post nonlinear mixtures. In Proceedings of the 11th European Signal Processing Conference (EUSIPCO), volume II, pages 11– 14. IEEE. Bach, F. and Jordan, M. (2004). Blind one-microphone speech separation: a spectral learning approach. In L. Saul, Y. Weiss, and L. Bottou, editors, Proceedings of the 17th Annual Conference on Neural Information Processing Systems (NIPS 2004), pages 65– 72. MIT Press, Cambridge, MA. Bach, F. and Jordan, M. (2005a). Predictive low-rank decomposition for kernel methods. In ICML '05 Proceedings of the 22nd International Conference on Machine Learning. ACM, New York. Bach, F. and Jordan, M. I. (2002). Kernel independent component analysis. Journal of Machine Learning Research, 3, 1– 48. Bach, F. R. and Jordan, M. I. (2005b). Predictive low-rank decomposition for kernel methods. In ICML '05 Proceedings of the 22nd International Conference on Machine Learning, pages 33– 40. ACM, New York. Bach, F. R., Lanckriet, G. R., and Jordan, M. I. (2004). Multiple kernel learning, conic duality, and the SMO algorithm. In ICML '04 Proceedings of the 21st International Conference on Machine Learning, page 6. ACM, New York. Bahl, P. and Padmanabhan, V. (2000). RADAR: a in-building RF based user location and tracking system. In IEEE INFOCOM '2000 Proceedings of the 19th Annual Joint Conference of the IEEE Computer and Communications Societies (Cat. No.00CH37064), pages 775– 784. IEEE Press. Bai, E.-W. (1998). An optimal two stage identification algorithm for Hammerstein–Wiener nonlinear systems. In Proceedings of the 1998 American Control Conference, volume 5, pages 2756– 2760. Bai, W., He, C., Jiang, L. G., and Li, X. X. (2003). Robust channel estimation in MIMO–OFDM systems. Electronics Letters, 39(2), 242– 244. Bajwa, W. U., Haupt, J., Sayeed, A. M., and Nowak, R. (2010). Compressed channel sensing: a new approach to estimating sparse multipath channels. Proceedings of the IEEE, 98(6), 1058– 1076. Baker, C. (1973). Joint measures and cross-covariance operators. Transactions of the American Mathematical Society, 186, 273– 289. G. Bakır, T. Hofmann, B. Schölkopf, A. Smola, B. Taskar, and S. Vishwanathan, editors (2007). Predicting Structured Data. MIT Press, Cambridge, MA. Balestrino, A., Landi, A., Ould-Zmirli, M., and Sani, L. (2001). Automatic nonlinear auto-tuning method for Hammerstein modeling of electrical drives. IEEE Transactions on Industrial Electronics, 48(3), 645– 655. Ball, J. E. and Bruce, L. M. (2007). Level set hyperspectral image classification using best band analysis. IEEE Transactions on Geoscience and Remote Sensing, 45(10), 3022– 3027. Banham, M. and Katsaggelos, A. (1997). Digital image restoration. IEEE Signal Processing Magazine, 14, 24– 41. Barker, M. and Rayens, W. (2003). Partial least squares for discrimination. Journal of Chemometrics, 17, 166– 173. Basseville, M. and Nikiforov, I. V. (1993). Detection of Abrupt Changes: Theory and Application. Prentice-Hall, Upper Saddle River, NJ. Bayro-Corrochano, E. J. and Arana-Daniel, N. (2010). Clifford support vector machines for classification, regression, and recurrence. IEEE Transactions on Neural Networks, 21(11), 1731– 1746. Belkin, M. and Niyogi, P. (2003). Laplacian eigenmaps for dimensionality reduction and data representation. Neural Computation, 15(6), 1373– 1396. Belkin, M. and Niyogi, P. (2004). Semi-supervised learning on Riemannian manifolds. Machine Learning, Special Issue on Clustering, 56, 209– 239. Belkin, M., Niyogi, P., and Sindhwani, V. (2006). Manifold regularization: a geometric framework for learning from labeled and unlabeled examples. Journal of Machine Learning Research, 7, 2399– 2434. Bell, A. J. and Sejnowski, T. J. (1995). An information-maximization approach to blind separation and blind deconvolution. Neural Computation, 7(6), 1129– 1159. Belouchrani, A. and Amin, M. G. (1998). Blind source separation based on time–frequency signal representations. IEEE Transactions on Signal Processing, 46(11), 2888– 2897. Ben-David, S., Blitzer, J., Crammer, K., and Pereira, O. (2007). Analysis of representations for domain adaptation. In B. Schölkopf, J. Platt, and T. Hofmann, editors, Advances in Neural Information Processing Systems 19, Proceedings of the 2006 Conference. MIT Press, Cambridge, MA. Bermejo, J., Antoranz, J., Burwash, I., Rojo-Álvarez, J. L., Moreno, M., García-Fernández, M., and Otto, C. (2002). In-vivo analysis of the instantaneous transvalvular pressure difference in aortic valve stenosis. implications of unsteady fluid-dynamics for the clinical assessment of disease severity. Journal of Heart Valve Disease, 11(4), 557– 566. Bicego, M., Ulaş, A., Castellani, U., Perina, A., Murino, V., Martins, A. F. T., Aguiar, P. M. Q., and Figueiredo, M. A. T. (2013). Combining information theoretic kernels with generative embeddings for classification. Neurocomputing, 101, 161– 169. Bickel, S., Brückner, M., and Scheffer, T. (2009). Discriminative learning under covariate shift. Journal of Machine Learning Research, 10, 2137– 2155. Billings, S. (1980). Identification of nonlinear systems: a survey. Proceedings of IEE, Part D, 127, 272– 285. Billings, S. A. and Fakhouri, S. Y. (1977). Identification of nonlinear systems using the Wiener model. Electronics Letters, 13(17), 502– 504. Billings, S. A. and Fakhouri, S. Y. (1979). Nonlinear system identification using the Hammerstein model. International Journal of System Sciences, 10(5), 567– 578. Billings, S. A. and Fakhouri, S. Y. (1982). Identification of systems containing linear dynamic and static nonlinear elements. Automatica, 18, 15– 26. Bimbot, F., Bonastre, J.-F., Fredouille, C., Gravier, G., Magrin-Chagnolleau, I., Meignier, S., Merlin, T., Ortega-García, J., Petrovska-Delacrétaz, D., and Reynolds, D. A. (2004). A tutorial on text-independent speaker verification. EURASIP Jounal of Applied Signal Processing, 2004, 430– 451. Bishop, C. (2006). Pattern Recognition and Machine Learning. Springer. Bishop, C. and Tipping, M. (2000). Variational relevance vector machines. In C. Boutilier and M. Goldszmidt, editors, Proceedings of 16th Conference on Uncertainty in Artificial Intelligence, pages 46– 53. Morgan Kaufmann, San Francisco, CA. Blankertz, B., Muller, K.-R., Curio, G., Vaughan, T., Schalk, G., Wolpaw, J., Schlogl, A., Neuper, C., Pfurtscheller, G., Hinterberger, T., Schroder, M., and Birbaumer, N. (2004). The BCI competition 2003: progress and perspectives in detection and discrimination of EEG single trials. IEEE Transactions on Biomedical Engineering, 51(6), 1044– 1051. Blaschko, M. and Lampert, C. (2008). Learning to localize objects with structured output regression. In D. Forsyth, P. Torr, and A. Zisserman, editors, Computer Vision: ECCV 2008, pages 2– 15. Springer. Blaschko, M., Shelton, J., Bartels, A., Lampert, C., and Gretton, A. (2011). Semi-supervised kernel canonical correlation analysis with application to human fMRI. Pattern Recognition Letters, 32, 1572– 1583. Blattberg, R. and Neslin, S. (1993). Sales promotion models. In J. Eliashberg and G. Lilien, editors, Handbooks in Operations Research and Management Science: Marketing Models, pages 553– 609. North-Holland, Amsterdam. Blitzer, J., McDonald, R., and Pereira, F. (2006). Domain adaptation with structural correspondence learning. In EMNLP '06 Proceedings of the 2006 Conference on Empirical Methods in Natural Language Processing, pages 120– 128. Association for Computational Linguistics, Stroudsburg, PA. Bloomfield, P. and Steiger, W. (1980). Least absolute deviations curve-fitting. SIAM Journal on Scientific Computing, 1(2), 290– 301. Bofill, P. and Zibulevsky, M. (2001). Underdetermined blind source separation using sparse representations. Signal Processing, 81(11), 2353– 2362. Bookstein, F. (1989). Principal warps: thin-plate splines and the decomposition of deformations. IEEE Transactions on Pattern Analysis and Machine Intelligence, 11(6), 567– 585. Bordes, A., Ertekin, S., Weston, J., and Bottou, L. (2005). Fast kernel classifiers with online and active learning. Journal of Machine Learning Research, 6, 1579– 1619. Borga, M., Landelius, T., and Knutsson, H. (1997). A unified approach to PCA, PLS, MLR and CCA . Technical Report LiTH-ISY-R, 1400-3902, Linköping University. Borgwardt, K., Gretton, A., Rasch, M., Kriegel, H.-P., Schoelkopf, B., and Smola, A. (2006). Integrating structured biological data by kernel maximum mean discrepancy. Bioinformatics (ISMB), 22(14), e49– e57. Bose, N. K. and Basu, S. (1978). Multidimensional systems theory: matrix Padé approximants. In 1978 IEEE Conference on Decision and Control including the 17th Symposium on Adaptive Processes, pages 653– 657. IEEE Press. Boser, B. E., Guyon, I., and Vapnik, V. N. (1992). A training algorithm for optimal margin classifiers. In COLT '92 Proceedings of the Fifth Annual Workshop on Computational Learning Theory, pages 144– 152. ACM. Bottou, L. and Bengio, Y. (1995). Convergence properties of the k-means algorithms. In G. Tesauro, D. S. Touretzky, and T. K. Leen, editors, Advances in Neural Information Processing Systems 7, pages 585– 592. MIT Press, Cambridge, MA. L. Bottou, O. Chapelle, D. DeCoste, and J. Weston, editors (2007). Large Scale Kernel Machines. MIT Press, Cambridge, MA. Bouboulis, P. and Theodoridis, S. (2010). The complex Gaussian kernel LMS algorithm. In K. Diamantaras, W. Duch, and L. Iliadis, editors, Artificial Neural Networks – ICANN 2010, volume 6353 of Lecture Notes in Computer Science, pages 11– 20. Springer, Berlin. Bouboulis, P. and Theodoridis, S. (2011). Extension of Wirtinger's calculus to reproducing kernel Hilbert spaces and the complex kernel LMS. IEEE Transactions on Signal Processing, 59(3), 964– 978. Bouboulis, P., Theodoridis, S., and Mavroforakis, M. (2012). The augmented complex kernel LMS. IEEE Transactions on Signal Processing, 60(9), 4962– 4967. Boyd, S. and Chua, L. (1985). Fading memory and the problem of approximating nonlinear operators with Volterra series. IEEE Transactions on Circuits and Systems, 32(11), 1150– 1161. Bradley, D. and Bagnell, J. (2008). Differentiable sparse coding. In D. Koller, D. Schuurmans, Y. Bengio, and L. Bottou, editors, NIPS'08 Proceedings of the 21st International Conference on Neural Information Processing Systems, pages 113– 120. Curran Associates. Bradley, P. S. and Mangasarian, O. L. (1998). Feature selection via concave minimization and support vector machines. In J. Shavlik, editor, ICML '98 Proceedings of the Fifteenth International Conference on Machine Learning, pages 82– 90. Morgan Kaufmann, San Francisco, CA. Braun, M. L., Buhmann, J. M., and Müller, K.-R. (2008). On relevant dimensions in kernel feature spaces. Journal of Machine Learning Research, 9, 1875– 1908. Bredensteiner, E. J. and Bennett, K. P. (1999). Multicategory classification by support vector machines. Computational Optimization and Applications, 12(1), 53– 79. Breiman, L. (1994). Bagging predictors. Technical Report 421, University of California at Berkley. Brilliant, M. B. (1958). Theory of the analysis of nonlinear systems. RLE Technical Report 345, MIT. Brinker, K. (2003). Incorporating diversity in active learning with support vector machines. In ICML'03 Proceedings of the Twentieth International Conference on International Conference on Machine Learning. AAAI Press. Bruls, J., Chou, C., Haverkamp, B., and Verhaegen, M. (1999). Linear and nonlinear system identification using separable least-squares. European Journal of Control Engineering Practice, 5(1), 116– 128. Bugallo, M. F., Martino, L., and Corander, J. (2015). Adaptive importance sampling in signal processing. Digital Signal Processing, 47, 36– 49. Burg, J. P. (1967). Maximum entropy spectral analysis. In Proceedings of 37th Meeting of Society of Exploration Geophysicists, Oklahoma City, OK. Burges, C. (1998). A tutorial on support vector machines for pattern recognition. Data Mining and Knowledge Discovery, 2(2), 1– 32. Burges, C. J. C. (1999). Geometry and invariance in kernel based methods. In B. Schölkopf, C. J. C. Burges, and A. J. Smola, editors, Advances in Kernel Methods – Support Vector Learning, pages 89– 116. MIT Press, Cambridge, MA. Burrus, C. S. and Parks, T. W. (1970). Time domain design of recursive digital filters. IEEE Transactions on Audio and Electroacoustics, 18(2), 137– 141. Butzer, P. L. and Stens, R. L. (1992). Sampling for not necessarily band-limited functions: a historical overview. SIAM Review, 34(1), 40– 53. Byrne, C. L. and Fitzgerald, R. (1983). An approximation theoretic approach to maximum entropy spectral analysis. IEEE Transactions on Acoustics, Speech and Signal Processing, 31(3), 734– 736. Cambanis, S., Huang, S., and Simons, G. (1981). On the theory of elliptically contoured distributions. Journal of Multivariate Analysis, 11(3), 368– 385. Campbell, C., Cristianini, N., and Smola, A. (2000). Query learning with large margin classifiers. In P. Langley, editor, ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning, pages 111 – 118. Morgan Kaufmann, San Francisco, CA. Camps-Valls, G. (2016). Kernel spectral angle mapper. Electronics Letters, 52(14), 1218– 1220. Camps-Valls, G. and Bruzzone, L. (2005). Kernel-based methods for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing, 43, 1351– 1362. G. Camps-Valls and L. Bruzzone, editors (2009). Kernel methods for Remote Sensing Data Analysis. Wiley & Sons, UK. Camps-Valls, G., Soria-Olivas, E., Pérez-Ruixo, J., Artés-Rodríguez, A., Pérez-Cruz, F., and Figueiras-Vidal, A. (2001). A profile-dependent kernel-based regression for cyclosporine concentration prediction. In Neural Information Processing Systems, NIPS'01. Workshop on New Directions in Kernel-based Learning Methods. Camps-Valls, G., Soria-Olivas, E., Pérez-Ruixo, J., Artés-Rodríguez, A., Pérez-Cruz, F., and Figueiras-Vidal, A. (2002). Cyclosporine concentration prediction using clustering and support vector regression methods. Electronics Letters, 38(12), 568– 570. Camps-Valls, G., Martínez-Ramón, M., Rojo-Álvarez, J. L., and Soria-Olivas, E. (2004). Robust γ-filter using support vector machines. Neurocomputing, 62, 493– 499. Camps-Valls, G., Gómez-Chova, L., Muñoz-Marí, J., Vila-Francés, J., and Calpe-Maravilla, J. (2006a). Composite kernels for hyperspectral image classification. IEEE Geoscience and Remote Sensing Letters, 3(1), 93– 97. Camps-Valls, G., Gómez-Chova, L., Vila-Francés, J., Amorós-López, J., Muñoz-Marí, J., and Calpe-Maravilla, J. (2006b). Retrieval of oceanic chlorophyll concentration with relevance vector machines. Remote Sensing of Environment, 105(1), 23– 33. Camps-Valls, G., Bruzzone, L., Rojo-Álvarez, J. L., and Melgani, F. (2006c). Robust support vector regression for biophysical parameter estimation from remotely sensed images. IEEE Geoscience and Remote Sensing Letters, 3(3), 339– 343. G. Camps-Valls, J. L. Rojo-Álvarez, and M. Martínez-Ramón, editors (2007). Kernel Methods in Bioengineering, Signal, and Image Processing. Idea Group Inc., Hershey, PA. Camps-Valls, G., Martínez-Ramón, M., Rojo-Álvarez, J. L., and Muñoz-Marí, J. (2007a). Non-linear system identification with composite relevance vector machines. IEEE Signal Processing Letters, 14(4), 279– 282. Camps-Valls, G., Bandos, T., and Zhou, D. (2007b). Semi-supervised graph-based hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing, 45(10), 2044– 3054. Camps-Valls, G., Gómez-Chova, L., Muñoz-Marí, J., Rojo-Álvarez, J. L., and Martínez-Ramón, M. (2008). Kernel-based framework for multitemporal and multisource remote sensing data classification and change detection. IEEE Transactions on Geoscience and Remote Sensing, 46(6), 1822– 1835. Camps-Valls, G., Muñoz-Marí, J., Gómez-Chova, L., Richter, K., and Calpe-Maravilla, J. (2009a). Biophysical parameter estimation with a semisupervised support vector machine. IEEE Geoscience and Remote Sensing Letters, 6(2), 248– 252. Camps-Valls, G., Muñoz-Marí, J., Martínez-Ramón, M., Requena-Carrion, J., and Rojo-Álvarez, J. L. (2009b). Learning non-linear time-scales with kernel gamma-filters. Neurocomputing, 72(4–6, SI), 1324– 1328. Candes, E. J. and Wakin, M. B. (2008). An introduction to compressive sampling. IEEE Signal Processing Magazine, 25(2), 21– 30. Candes, E. J., Romberg, J., and Tao, T. (2006). Robust uncertainty principles: exact signal reconstruction from highly incomplete frequency information. IEEE Transactions on Information Theory, 52(2), 489– 509. Cao, L. J., Keerthi, S. S., Ong, C.-J., Zhang, J. Q., Periyathamby, U., Fu, X. J., and Lee, H. P. (2006). Parallel sequential minimal optimization for the training of support vector machines. IEEE Transactions on Neural Networks, 17, 1039– 1049. Capon, J. (1969). High-resolution frequency-wavenumber spectrum analysis. Proceedings of the IEEE, 57(8), 1408– 1418. Cardoso, J. (1998). Blind signal separation: statistical principles. Proceedings of the IEEE, 9(10), 2009– 2025. Cardoso, J.-F. and Souloumiac, A. (1993). Blind beamforming for non-Gaussian signals. IEE Proceedings F – Radar and Signal Processing, 140(6), 362– 370. M. Casdagli and S. Eubank, editors (1992). Nonlinear Modeling and Forecasting, volume XII. Addison-Wesley, Reading, MA. Cawley, G. C. and Talbot, N. L. C. (2002). Reduced rank kernel ridge regression. Neural Processing Letters, 16(3), 293– 302. Chandola, V., Banerjee, A., and Kumar, V. (2009). Anomaly detection: a survey. ACM Compututing Surveys, 41(3), 15:1– 15:58. Chang, C.-C. and Lin, C.-J. (2002). Training nu-support vector regression: theory and algorithms. Neural Computation, 14(8), 1959– 1978. Chang, E., Zhu, K., Wang, H., Bai, H., Li, J., Qiu, Z., and Cui, H. (2008). Parallelizing support vector machines on distributed computers. In J. C. Platt, D. Koller, Y. Singer, and S. Roweis, editors, Advances in Neural Information Processing Systems 20, pages 257– 264. MIT Press, Cambridge, MA. Chapelle, O. (2007). Training a support vector machine in the primal. Neural Computation, 19(5), 1155– 1178. Chapelle, O., Weston, J., and Schölkopf, B. (2003). Cluster kernels for semi-supervised learning. In S. Becker, S. Thrun, and K. Obermayer, editors, Advances in Neural Information Processing Systems 15, pages 601– 608. MIT Press, Cambridge, MA. Chapelle, O., Schölkopf, B., and Zien, A. (2006). Semi-Supervised Learning. MIT Press, Cambridge, MA. Chen, B., Zhao, S., Zhu, P., and Príncipe, J. C. (2012). Quantized kernel least mean square algorithm. IEEE Transactions on Neural Networks and Learning Systems, 23(1), 22– 32. Chen, H.-W. (1995). Modeling and identification of parallel nonlinear systems: structural classification and parameter estimation methods. Proceedings of the IEEE, 83(1), 39– 66. Chen, S., Billings, S. A., and Luo, W. (1989). Orthogonal least squares methods and their application to non-linear system identification. International Journal of Control, 50, 1873– 1896. Chen, S., Gunn, S., and Harris, C. (2000). Decision feedback equalizer design using support vector machines. IEE Proceedings – Vision, Image and Signal Processing, 147(3), 213– 219. Chen, S., Sanmigan, A. K., and Hanzo, L. (2001a). Adaptive mutiuser receiver using a support vector machine technique. In IEEE VTS 53rd Vehicular Technology Conference, Spring 2001. Proceedings (Cat. No.01CH37202), pages 604– 608. IEEE Press, Piscataway, NJ. Chen, S., Sanmigan, A. K., and Hanzo, L. (2001b). Support vector machine multiuser receiver for DS-CDMA signals in multipath channels. Neural Networks, 12(3), 604– 611. Chen, S. S., Donoho, D. L., Michael, and Saunders, A. (1998). Atomic decomposition by basis pursuit. SIAM Journal on Scientific Computing, 20, 33– 61. Cheng, M., Pun, C.-M., and Tang, Y. Y. (2014). Nonnegative class-specific entropy component analysis with adaptive step search criterion. Pattern Analysis and Applications, 17(1), 113– 127. Cheng, S. and Shih, F. Y. (2007). An improved incremental training algorithm for support vector machines using active query. Pattern Recognition, 40, 964 – 971. Cherkassky, V. and Ma, Y. (2004). Practical selection of SVM parameters and noise estimation for SVM regression. Neural Networks, 17(1)" @default.
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- W4211036582 title "References" @default.
- W4211036582 cites W100313625 @default.
- W4211036582 cites W124844327 @default.
- W4211036582 cites W1423766661 @default.
- W4211036582 cites W1479807131 @default.
- W4211036582 cites W1480376833 @default.
- W4211036582 cites W1487322600 @default.
- W4211036582 cites W1489704256 @default.
- W4211036582 cites W1491334018 @default.
- W4211036582 cites W1496317909 @default.
- W4211036582 cites W1500583457 @default.
- W4211036582 cites W1501131549 @default.
- W4211036582 cites W1510073064 @default.
- W4211036582 cites W1515020792 @default.
- W4211036582 cites W1515024463 @default.
- W4211036582 cites W1522853647 @default.
- W4211036582 cites W1525954826 @default.
- W4211036582 cites W1526741802 @default.
- W4211036582 cites W1527116762 @default.
- W4211036582 cites W1528361845 @default.
- W4211036582 cites W1539591727 @default.
- W4211036582 cites W1546368884 @default.
- W4211036582 cites W1554422878 @default.
- W4211036582 cites W1580801000 @default.
- W4211036582 cites W1582898906 @default.
- W4211036582 cites W1587294646 @default.
- W4211036582 cites W1592082209 @default.
- W4211036582 cites W1596717185 @default.
- W4211036582 cites W1604408772 @default.
- W4211036582 cites W1626653188 @default.
- W4211036582 cites W1636503046 @default.
- W4211036582 cites W1638081485 @default.
- W4211036582 cites W1648445109 @default.
- W4211036582 cites W1663440244 @default.
- W4211036582 cites W1672071289 @default.
- W4211036582 cites W1676820704 @default.
- W4211036582 cites W1761010383 @default.
- W4211036582 cites W1797539709 @default.
- W4211036582 cites W1839113334 @default.
- W4211036582 cites W187762159 @default.
- W4211036582 cites W1890834058 @default.
- W4211036582 cites W1964357740 @default.
- W4211036582 cites W1965533816 @default.
- W4211036582 cites W1965747377 @default.
- W4211036582 cites W1965748119 @default.
- W4211036582 cites W1965870577 @default.
- W4211036582 cites W1966347620 @default.
- W4211036582 cites W1966566667 @default.
- W4211036582 cites W1966949944 @default.
- W4211036582 cites W1967005434 @default.
- W4211036582 cites W1967042859 @default.
- W4211036582 cites W1967134206 @default.
- W4211036582 cites W1968986632 @default.
- W4211036582 cites W1969695669 @default.
- W4211036582 cites W1970062266 @default.
- W4211036582 cites W1970099214 @default.
- W4211036582 cites W1971527405 @default.
- W4211036582 cites W1971571088 @default.
- W4211036582 cites W1972488631 @default.
- W4211036582 cites W1973407458 @default.
- W4211036582 cites W1973914465 @default.
- W4211036582 cites W1974618482 @default.
- W4211036582 cites W1974956622 @default.
- W4211036582 cites W1975277534 @default.
- W4211036582 cites W1975900269 @default.
- W4211036582 cites W1977013487 @default.
- W4211036582 cites W1978648390 @default.
- W4211036582 cites W1979162050 @default.
- W4211036582 cites W1979955444 @default.
- W4211036582 cites W1980446475 @default.
- W4211036582 cites W1980759970 @default.
- W4211036582 cites W1981025032 @default.
- W4211036582 cites W1982175152 @default.
- W4211036582 cites W1983500014 @default.
- W4211036582 cites W1984159596 @default.
- W4211036582 cites W1986280275 @default.
- W4211036582 cites W1986931325 @default.
- W4211036582 cites W1987450470 @default.
- W4211036582 cites W1987932920 @default.
- W4211036582 cites W1988130470 @default.
- W4211036582 cites W1988747891 @default.
- W4211036582 cites W1988824261 @default.
- W4211036582 cites W1990044269 @default.
- W4211036582 cites W1990517717 @default.
- W4211036582 cites W1990663050 @default.
- W4211036582 cites W1993480174 @default.
- W4211036582 cites W1994393928 @default.
- W4211036582 cites W1996071780 @default.
- W4211036582 cites W1996278632 @default.
- W4211036582 cites W1997175528 @default.
- W4211036582 cites W1997534378 @default.
- W4211036582 cites W1997903011 @default.
- W4211036582 cites W2000804746 @default.
- W4211036582 cites W2001141328 @default.