Computational Learning Theory

Volume 2111 of the series Lecture Notes in Computer Science pp 224-240


Rademacher and Gaussian Complexities: Risk Bounds and Structural Results

  • Peter L. BartlettAffiliated withBIOwulf Technologies
  • , Shahar MendelsonAffiliated withResearch School of Information Sciences and Engineering, Australian National University

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We investigate the use of certain data-dependent estimates of the complexity of a function class, called Rademacher and gaussian complexities. In a decision theoretic setting, we prove general risk bounds in terms of these complexities. We consider function classes that can be expressed as combinations of functions from basis classes and show how the Rademacher and gaussian complexities of such a function class can be bounded in terms of the complexity of the basis classes.We give examples of the application of these techniques in finding data-dependent risk bounds for decision trees, neural networks and support vector machines.