Statements (48)
| Predicate | Object |
|---|---|
| gptkbp:instanceOf |
gptkb:mathematical_concept
|
| gptkbp:defines |
Hilbert space of functions in which evaluation at each point is a continuous linear functional
|
| gptkbp:field |
gptkb:machine_learning
gptkb:mathematics functional analysis statistics |
| gptkbp:firstDescribed |
1900s
|
| gptkbp:hasApplication |
gptkb:probability_theory
gptkb:signal_processing time series analysis approximation theory pattern recognition regularization |
| gptkbp:hasFeature |
enables feature mapping
enables function approximation enables inner product computation via kernel enables kernel methods enables non-linear learning enables regularization in learning enables signal reconstruction enables statistical inference evaluation functional is bounded every function is determined by its values at all points |
| gptkbp:hasKernel |
reproducing kernel
|
| gptkbp:hasProperty |
inner product
complete separable reproducing kernel |
| https://www.w3.org/2000/01/rdf-schema#label |
RKHS
|
| gptkbp:isA |
gptkb:Hilbert_space
|
| gptkbp:originatedIn |
N. Aronszajn
S. Bergman S. Bochner |
| gptkbp:relatedTo |
gptkb:Hilbert_space
gptkb:L2_space gptkb:Mercer's_theorem gptkb:Sobolev_space gptkb:Moore-Aronszajn_theorem feature space kernel trick positive definite kernel |
| gptkbp:standsFor |
Reproducing Kernel Hilbert Space
|
| gptkbp:usedIn |
gptkb:Gaussian_processes
gptkb:kernel_methods learning theory support vector machines |
| gptkbp:bfsParent |
gptkb:reproducing_kernel_Hilbert_space
|
| gptkbp:bfsLayer |
6
|