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Generalization and Generalizability Measures

Published: 01 January 1999 Publication History

Abstract

In this paper, we define the generalization problem, summarize various approaches in generalization, identify the credit assignment problem, and present the problem and some solutions in measuring generalizability. We discuss anomalies in the ordering of hypotheses in a subdomain when performance is normalized and averaged, and show conditions under which anomalies can be eliminated. To generalize performance across subdomains, we present a measure called probability of win that measures the probability whether one hypothesis is better than another. Finally, we discuss some limitations in using probabilities of win and illustrate their application in finding new parameter values for TimberWolf, a package for VLSI cell placement and routing.

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Published In

cover image IEEE Transactions on Knowledge and Data Engineering
IEEE Transactions on Knowledge and Data Engineering  Volume 11, Issue 1
January 1999
262 pages

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IEEE Educational Activities Department

United States

Publication History

Published: 01 January 1999

Author Tags

  1. Anomalies in generalization
  2. VLSI cell placement and routing.
  3. credit assignment problem generalization
  4. machine learning
  5. probability of win
  6. subdomains

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