• English
  • Deutsch
  • Log In
    Password Login
    Research Outputs
    Fundings & Projects
    Researchers
    Institutes
    Statistics
Repository logo
Fraunhofer-Gesellschaft
  1. Home
  2. Fraunhofer-Gesellschaft
  3. Konferenzschrift
  4. Learning to predict the leave-one-out error of kernel based classifiers
 
  • Details
  • Full
Options
2001
Conference Paper
Title

Learning to predict the leave-one-out error of kernel based classifiers

Abstract
We propose an algorithm to predict the leave-one-out (LOO) error for kernel based classifiers. To achieve this goal with computational efficiency, we cast the LOO error approximation task into a classification problem. This means that we need to learn a classification of whether or not a given training sample - if left out of the data set - would be misclassified. For this learning task, simple data dependent features are proposed, inspired by geometrical intuition. Our approach allows to reliably select a good model as demonstrated in simulations on Support Vector and Linear Programming Machines. Comparisons to existing learning theoretical bounds, e,g. the span bound, are given for various model selection scenarios.
Author(s)
Tsuda, K.
Rätsch, G.
Mika, S.
Müller, K.-R.
Mainwork
Artificial neural networks  
Conference
International Conference on Artificial Neural Networks (ICANN) 2001  
Language
English
FIRST
  • Cookie settings
  • Imprint
  • Privacy policy
  • Api
  • Contact
© 2024