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Conclusion: What is Important in Learning Theory?

In: The Nature of Statistical Learning Theory

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  • Vladimir N. Vapnik

    (AT&T Bell Laboratories)

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.

Suggested Citation

  • Vladimir N. Vapnik, 1995. "Conclusion: What is Important in Learning Theory?," Springer Books, in: The Nature of Statistical Learning Theory, pages 167-175, Springer.
    • Handle: RePEc:spr:sprchp:978-1-4757-2440-0_7
    DOI: 10.1007/978-1-4757-2440-0_7
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