Consistency of Learning Processes

Vladimir N. Vapnik
Additional contact information
Vladimir N. Vapnik: AT&T Bell Laboratories

Chapter Chapter 2 in The Nature of Statistical Learning Theory, 1995, pp 33-64 from Springer

Abstract: Abstract The goal of this part of the theory is to describe the conceptual model for learning processes that are based on the Empirical Risk Minimization inductive principle. This part of the theory has to explain when a learning machine that minimizes empirical risk can achieve a small value of actual risk (can generalize) and when it can not. In other words, the goal of this part is to describe the necessary and sufficient conditions for the consistency of learning processes that minimizes the empirical risk.

Keywords: Probability Measure; Indicator Function; Uniform Convergence; Elementary Event; Inductive Inference (search for similar items in EconPapers)
Date: 1995
References: Add references at CitEc
Citations:

There are no downloads for this item, see the EconPapers FAQ for hints about obtaining it.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:sprchp:978-1-4757-2440-0_3

Ordering information: This item can be ordered from
http://www.springer.com/9781475724400

DOI: 10.1007/978-1-4757-2440-0_3

Access Statistics for this chapter

More chapters in Springer Books from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().