References to Object, Functional and Structured Data

• M. A. Álvarez, L. Rosasco and N. D. Lawrence, Kernels for vector-valued functions: a review,  tech report, 2011.
• A. Argyriou, M. Pontil, and C.A. Micchelli, When is there a representer theorem? Vector versus matrix regularizers, Journal of Machine Learning Research, 10:2507-2529, 2009.
• G. Bakir, T. Hofmann, B. Schölkopf, A. Smola, B. Taskar and S. Vishwanathan (Eds.), Predicting Structured Data, MIT Press, 2007.
• C. Brouard, F. d’Alche-Buc and M. Szafranski, Semi-supervised Penalized Output Kernel Regression for Link Prediction, in Proceedings of the 28^th International Conference on Machine Learning (ICML 2011), Bellevue, WA, USA, 2011.
• A. Caponnetto, M. Pontil, C.A. Micchelli and Y. Ying, Universal multi-task kernels, Journal of Machine Learning Research, 9:1615-1646, 2008.
• S. Dabo-Niang and F. Ferraty (Eds.), Functional and Operatorial Statistics, Springer-Verlag, New-York, 2008.
• P. Geurts, L. Wehenkel and F. d’Alché-Buc, Kernelizing outputs of tree-based methods, in Proceedings of the 23^rd International Conference on Machine Learning (ICML 2006), Pittsburgh, PA, USA, 2006. ACM 2006, pp. 345-352.
• P. Geurts, L. Wehenkel and F. d’Alché-Buc, Gradient Boosting for Kernelized Output Spaces, in Proceedings of the 24^th International Conference on Machine Learning (ICML 2007), Corvallis, Oregon, USA, 2007.
• F. Ferraty, A. Laksaci, A. Tadj and P. Vieu, Kernel regression with functional response, Electronic Journal of Statistics, 5, 159-171, 2011.
• S. Jung, M. Foskey and J. S. Marron, Principal Arc Analysis on direct product manifolds, The Annals of Applied Statistics, 5, 578-603,2011.
• H. Kadri, A. Rabaoui, P. Preux, E. Duflos and A. Rakotomamonjy, Functional Regularized Least Squares Classication with Operator-valued Kernels, in Proceedings of the 28^th International Conference on Machine Learning (ICML 2011), Bellevue, WA, USA, 2011.
• H. Kadri, E. Duflos, P. Preux, S. Canu and M. Davy, Nonlinear functional regression: a functional RKHS approach. In Proceedings of the 13^th International Conference on Artificial Intelligence and Statistics (AISTATS), Italy, 2010.
• T. Kato, Perturbation theory for linear operators, Springer-Verlag, Berlin, 1966.
• C.A. Micchelli and M. Pontil, On learning vector-valued functions, Neural Computation, 17:177-204, 2005.
• J. O. Ramsay and B. W. Silverman, Functional Data Analysis, Springer-Verlag, 2nd ed., 2005.

There is a workshop for this: 

Object, functional and structured data: towards next generation kernel-based methods – ICML 2012 Workshop

 

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