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- W2234882433 abstract "In this paper we consider the problems of supervised classification and regression in the case where attributes and labels are functions: a data is represented by a set of functions, and the label is also a function. We focus on the use of reproducing kernel Hilbert space theory to learn from such functional data. Basic concepts and properties of kernel-based learning are extended to include the estimation of function-valued functions. In this setting, the representer theorem is restated, a set of rigorously defined infinite-dimensional operator-valued kernels that can be valuably applied when the data are functions is described, and a learning algorithm for nonlinear functional data analysis is introduced. The methodology is illustrated through speech and audio signal processing experiments." @default.
- W2234882433 created "2016-06-24" @default.
- W2234882433 creator A5017956425 @default.
- W2234882433 creator A5031137438 @default.
- W2234882433 creator A5038633752 @default.
- W2234882433 creator A5048394257 @default.
- W2234882433 creator A5079506428 @default.
- W2234882433 creator A5087891858 @default.
- W2234882433 date "2016-01-01" @default.
- W2234882433 modified "2023-10-14" @default.
- W2234882433 title "Operator-valued Kernels for Learning from Functional Response Data" @default.
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