A Bayesian approach to localized multi-kernel learning using the relevance vector machine

@article{Close2011ABA,
  title={A Bayesian approach to localized multi-kernel learning using the relevance vector machine},
  author={Ryan Close and Joseph N. Wilson and Paul D. Gader},
  journal={2011 IEEE International Geoscience and Remote Sensing Symposium},
  year={2011},
  pages={1103-1106}
}
Multi-kernel learning has become a popular method to allow classification models greater flexibility in representing the relationships between data points. This approach has evolved into localized multi-kernel learning, which creates classification models that have the ability to adapt to a multi-scale feature-space. The advantages of such an approach are often hampered by additional parameters and hyper-parameters involved in creating this model, not to mention the greater likelihood of over… CONTINUE READING

References

Publications referenced by this paper.
Showing 1-10 of 11 references

The Relevance Vector Machine

NIPS • 1999
View 3 Excerpts
Highly Influenced

A Genetic Multiple Kernel Relevance Vector Regression Approach

2010 Second International Workshop on Education Technology and Computer Science • 2010
View 1 Excerpt

Localized Multiple Kernel Regression

2010 20th International Conference on Pattern Recognition • 2010
View 1 Excerpt

Local Ensemble Kernel Learning for Object Category Recognition

2007 IEEE Conference on Computer Vision and Pattern Recognition • 2007

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