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News/Structural interpretability in SVMs with truncated orthogonal polynomial kernels
arxiv
PublishedApril 18, 2026 at 4:00 AM
—neutral

Structural interpretability in SVMs with truncated orthogonal polynomial kernels

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Publisher summary· verbatim

arXiv:2604.15285v1 Announce Type: cross Abstract: We study post-training interpretability for Support Vector Machines (SVMs) built from truncated orthogonal polynomial kernels. Since the associated reproducing kernel Hilbert space is finite-dimensional and admits an explicit tensor-product orthonorm

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Mentioned models
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    Support Vector Machines (SVMs)
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#machine-learning#interpretability#kernel-methods

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Mentioned models
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  • 01
    Support Vector Machines (SVMs)
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arxiv
Read original ↗All from arxiv →
Tags
03
#machine-learning#interpretability#kernel-methods

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