Conclusion: What is Important in Learning Theory?

Vladimir N. Vapnik
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Vladimir N. Vapnik: AT&T Bell Laboratories

A chapter in The Nature of Statistical Learning Theory, 1995, pp 167-175 from Springer

Abstract: 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 (search for similar items in EconPapers)
Date: 1995
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DOI: 10.1007/978-1-4757-2440-0_7

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