Corpus ID: 221151560

Learning Theory: An Approximation Theory Viewpoint

@inproceedings{Cucker2007LearningTA,
  title={Learning Theory: An Approximation Theory Viewpoint},
  author={F. Cucker and Ding-Xuan Zhou},
  year={2007}
}
Preface Foreword 1. The framework of learning 2. Basic hypothesis spaces 3. Estimating the sample error 4. Polynomial decay approximation error 5. Estimating covering numbers 6. Logarithmic decay approximation error 7. On the bias-variance problem 8. Regularization 9. Support vector machines for classification 10. General regularized classifiers Bibliography Index. 
162 Citations
Learning Rates of Least-Square Regularized Regression
  • 210
  • PDF
Coefficient Regularized Algorithms for Learning and Classification
Bound the learning rates with generalized gradients
  • 4
  • PDF
Integral Operator Approach to Learning Theory with Unbounded Sampling
  • 12
  • PDF
A note on application of integral operator in learning theory
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Iterative Regularization for Learning with Convex Loss Functions
  • 25
  • PDF
Analysis of Regression Algorithms with Unbounded Sampling
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