Adaptive Kernel Based Machine Learning Methods

Abstract

Research results obtained from this project address the kernel selection problem in machine learning. Specifically, motivated from the need of updating the current operator-valued reproducing kernel in multi-task learning when underfitting or overfitting occurs, we studied the construction of a refinement kernel for a given operator-valued reproducing kernel such that the vector-valued reproducing kernel Hilbert space of the refinement kernel contains that of the given kernel as a subspace. We also developed a complete characterization of multi-task finite rank kernels in terms of the positivity of what we call its associated characteristic operator

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Document Details

Document Type
Technical Report
Publication Date
Oct 15, 2012
Accession Number
ADA588768

Entities

People

  • Yuesheng Xu

Organizations

  • Syracuse University

Tags

Communities of Interest

  • Autonomy

DTIC Thesaurus Topics

  • Air Force
  • Air Force Research Laboratories
  • Algorithms
  • Boundaries
  • Boundary Value Problems
  • Computational Science
  • Convergence
  • Department Of Defense
  • Equations
  • Finite Element Analysis
  • Hilbert Space
  • Image Processing
  • Integral Equations
  • Integrals
  • Learning
  • Machine Learning
  • X-Ray Computed Tomography

Fields of Study

  • Computer science

Readers

  • Linear Algebra
  • Neural Network Machine Learning.

Technology Areas

  • AI & ML
  • AI & ML - Machine Learning Algorithms
  • Space