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A Psychophysics Paradigm for the Collection and Analysis of Similarity Judgments
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Does the cost function matter in Bayes decision rule?

Ralf Schlü ter1, Markus Nussbaum-Thom, Hermann Ney

  • 1Lehrstuhl für Informatik 6, Computer Science Department, RWTH Aachen University, Ahornstr. 55, Aachen 52074, Germany. schlueter@cs.rwth-aachen.de

IEEE Transactions on Pattern Analysis and Machine Intelligence
|August 17, 2011
PubMed
Summary
This summary is machine-generated.

This study analyzes the relationship between minimizing string errors and symbol errors in pattern recognition tasks. It derives conditions where different cost functions yield similar results, improving recognition system evaluation.

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Area of Science:

  • Pattern Recognition
  • Machine Learning
  • Information Theory

Background:

  • Pattern recognition tasks like ASR and OCR often face a conflict between minimizing string sequence errors and individual symbol errors.
  • The Bayes decision rule is typically used for string error minimization, but symbol error rate is the practical evaluation metric.

Purpose of the Study:

  • To analyze the relationship between string (0-1) and symbol (metric) cost functions within the Bayes decision rule.
  • To derive fundamental analytic results for conditions where these cost functions lead to the same decisions or limited costs.

Main Methods:

  • Derivation of analytic results for the Bayes decision rule with different cost functions.
  • Testing conditions for decision equivalence and limited costs.
  • Simulation of string recognition problems using Levenshtein distance.

Main Results:

  • Simple, testable conditions are derived for when metric and 0-1 cost functions yield identical decisions.
  • These conditions are independent of underlying distribution structures.
  • Simulations confirm that improvements are significant when initial error rates are high.

Conclusions:

  • The study provides a theoretical framework for reconciling string and symbol error minimization in pattern recognition.
  • The derived conditions offer practical insights for designing and evaluating recognition systems.
  • The findings suggest potential for substantial performance gains in high-error scenarios.