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Operator-Valued Kernels for Nonparametric Operator Estimation
Abstract : We consider the supervised learning problem when both covariates and responses are real functions rather than scalars or finite dimensional vectors. In this setting, we aim at developing a sound and effective nonparametric operator estimation approach based on optimal approximation in reproducing kernel Hilbert spaces of function-valued functions. In a first step, we exhibit a class of operator-valued kernels that perform the mapping between two spaces of functions: this is the first contribution of this paper. Then, we show how to solve the problem of minimizing a regularized functional without discretizing covariate and target functions. Finally, we apply this framework to a standard functional regression problem.
Cited literature [27 references]
https://hal.inria.fr/inria-00587649
Contributor : Hachem Kadri <>
Submitted on : Thursday, April 21, 2011 - 5:08:30 PM
Last modification on : Tuesday, November 24, 2020 - 2:18:23 PM
Long-term archiving on: : Friday, July 22, 2011 - 2:42:14 AM
Contributor : Hachem Kadri <>
Submitted on : Thursday, April 21, 2011 - 5:08:30 PM
Last modification on : Tuesday, November 24, 2020 - 2:18:23 PM
Long-term archiving on: : Friday, July 22, 2011 - 2:42:14 AM
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RR-7607.pdf
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