Advertisement

Conclusion: What is Important in Learning Theory?

  • Vladimir N. Vapnik

Abstract

In the beginning of this book we postulated (without any discussion) that learning is a problem of function estimation on the basis of empirical data. To solve this problem we used a classical inductive principle — the ERM principle. Later, however, we introduced a new principle — the SRM principle. Nevertheless, the general understanding of the problem remains based on the statistics of large samples: the goal is to derive the rule that possesses the lowest risk. The goal of obtaining the “lowest risk” reflects the philosophy of large sample size statistics: the rule with low risk is good because if we use this rule for a large test set, with high probability, the means of losses will be small.

Keywords

Growth Function Generalization Ability Empirical Risk Pattern Recognition Problem Inductive Principle 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

Copyright information

© Springer Science+Business Media New York 1995

Authors and Affiliations

  • Vladimir N. Vapnik
    • 1
  1. 1.AT&T Bell LaboratoriesHolmdelUSA

Personalised recommendations