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Bayesian interpretation of kernel regularization

Known as: Bayesian interpretation of regularization 
In machine learning, kernel methods arise from the assumption of an inner product space or similarity structure on inputs. For some such methods… Expand
Wikipedia

Related topics

Related topics

8 relations
Gaussian processGramian matrixHilbert spaceKernel methods for vector output
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Broader (1)

Machine learning

Papers overview

Semantic Scholar uses AI to extract papers important to this topic.
2016
2016
Regularized linear system identification using atomic, nuclear and kernel-based norms: The role of the stability constraint
Inspired by ideas taken from the machine learning literature, new regularization techniques have been recently introduced in… Expand
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2016
2016
A new kernel-based approach to hybrid system identification
All the approaches for hybrid system identification appeared in the literature assume that model complexity is known. Popular… Expand
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2015
2015
Identification of hybrid systems using stable spline kernels
  • G. Pillonetto
  • IEEE 25th International Workshop on Machine…
  • 2015
    • Corpus ID: 11342271
All the approaches for identification of hybrid systems appeared in the literature assume known the model complexity. Widely used… Expand
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Highly Cited
2004
Highly Cited
2004
Bayesian modeling of uncertainty in low-level vision
  • R. Szeliski
  • International Journal of Computer Vision
  • 2004
    • Corpus ID: 8433503
The need for error modeling, multisensor fusion, and robust algorithms is becoming increasingly recognized in computer vision… Expand
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2003
2003
Regularization networks for inverse problems: A state-space approach
Linear inverse problems with discrete data are equivalent to the estimation of the continuous-time input of a linear dynamical… Expand
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