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On a class of support vector kernels based on frames in function Hilbert spaces.

J B Gao1, C J Harris, S R Gunn

  • 1Image, Speech and Intelligent System Research Group, Department of Electronics and Computer Science, University of Southampton, Southampton SO17 1BJ, UK.

Neural Computation
|August 23, 2001
PubMed
Summary
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This study introduces a novel class of kernel functions derived from frames in function Hilbert spaces. These new kernels offer potential advancements for kernel-based machine learning techniques.

Area of Science:

  • Machine Learning
  • Functional Analysis

Background:

  • Kernel-based methods like Support Vector Machines and Gaussian Processes are increasingly popular.
  • These techniques share underlying relationships centered on the kernel function.

Purpose of the Study:

  • To explore a new class of kernel functions.
  • To investigate kernels derived from frames in function Hilbert spaces.

Main Methods:

  • Utilizing the mathematical framework of frames in function Hilbert spaces.
  • Deriving new kernel functions based on these frames.

Main Results:

  • A novel class of kernel functions has been identified.
  • These kernels are derived from the structure of frames.

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Conclusions:

  • The newly derived kernel functions offer a new perspective on kernel-based methods.
  • This research expands the toolkit for kernel function design in machine learning.