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Journal of Machine Learning Research,-0001,7(95):2651−2667
-1年11月30日
In this paper we investigate conditions on the features of a continuous kernel so that it may approximate an arbitrary continuous target function uniformly on any compact subset of the input space. A number of concrete examples are given of kernels with this universal approximating property.
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Journal of Machine Learning Research,-0001,8(71):2083−2120
-1年11月30日
Motivated by mathematical learning from training data, we introduce the notion of refinable kernels. Various characterizations of refinable kernels are presented. The concept of refinable kernels leads to the introduction of wavelet-like reproducing kernels. We also investigate a refinable kernel that forms a Riesz basis. In particular, we characterize refinable translation invariant kernels, and refinable kernels defined by refinable functions. This study leads to multiresolution analysis of reproducing kernel Hilbert spaces.
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【期刊论文】Refinement of Reproducing Kernels
The Journal of Machine Learning Research,-0001,10():107-140
-1年11月30日
We continue our recent study on constructing a refinement kernel for a given kernel so that the reproducing kernel Hilbert space associated with the refinement kernel contains that with the original kernel as a subspace. To motivate this study, we first develop a refinement kernel method for learning, which gives an efficient algorithm for updating a learning predictor. Several characterizations of refinement kernels are then presented. It is shown that a nontrivial refinement kernel for a given kernel always exists if the input space has an infinite cardinal number. Refinement kernels for translation invariant kernels and Hilbert-Schmidt kernels are investigated. Various concrete examples are provided.
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【期刊论文】Reproducing Kernel Banach Spaces for Machine Learning
The Journal of Machine Learning Research,-0001,10():2741-2775
-1年11月30日
We introduce the notion of reproducing kernel Banach spaces (RKBS) and study special semi-inner-product RKBS by making use of semi-inner-products and the duality mapping. Properties of an RKBS and its reproducing kernel are investigated. As applications, we develop in the framework of RKBS standard learning schemes including minimal norm interpolation, regularization network, support vector machines, and kernel principal component analysis. In particular, existence, uniqueness and representer theorems are established.
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【期刊论文】Frames, Riesz bases, and sampling expansions in Banach spaces via semi-inner products
Applied and Computational Harmonic Analysis,2011,31(1):1-25
2011年07月01日
Frames in a Banach space were defined as a sequence in its dual space ⁎ in some recent references. We propose to define them as a collection of elements in by making use of semi-inner products. Classical theory on frames and Riesz bases is generalized under this new perspective. We then aim at establishing the Shannon sampling theorem in Banach spaces. The existence of such expansions in translation invariant reproducing kernel Hilbert and Banach spaces is discussed.
Frames Riesz bases Bessel sequences Riesz–Fischer sequences Banach spaces Semi-inner products Duality mappings Shannonʼs sampling expansions Reproducing kernel Banach spaces Reproducing kernel Hilbert spaces Gaussian kernels
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