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Related Experiment Videos

Approximation bounds for some sparse kernel regression algorithms.

Tong Zhang1

  • 1IBM T J Watson Research Center, Yorktown Heights, NY 10598, USA. tzhang@watson.ibm.com

Neural Computation
|December 19, 2002
PubMed
Summary
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Gaussian processes are powerful for regression but computationally costly. This study analyzes sparse methods to improve efficiency, providing approximation bounds for these Gaussian process techniques.

Area of Science:

  • Machine Learning
  • Statistical Modeling

Background:

  • Gaussian processes (GPs) are effective for regression tasks.
  • High computational cost limits GP scalability.

Purpose of the Study:

  • Investigate properties of sparse regression algorithms for Gaussian processes.
  • Analyze computational cost reduction in GPs.

Main Methods:

  • Examined sparse approximation algorithms for Gaussian processes.
  • Derived theoretical approximation bounds.

Main Results:

  • Obtained quantitative approximation bounds for sparse GP regression.
  • Compared the performance and bounds with existing methods.

Conclusions:

Related Experiment Videos

  • Sparse Gaussian process methods offer a computationally efficient alternative.
  • The derived bounds provide insights into the accuracy of approximations.