OcrV1, Main, Exploration, bibRecord, 000265

Does the cost function matter in Bayes decision rule?

Identifieur interne : 000265 ( Main/Exploration ); précédent : 000264; suivant : 000266

Does the cost function matter in Bayes decision rule?

Auteurs : Ralf Schlü Ter [Allemagne] ; Markus Nussbaum-Thom ; Hermann Ney

Source :

IEEE transactions on pattern analysis and machine intelligence [ 1939-3539 ] ; 2012.

RBID : pubmed:21844628

English descriptors

KwdEn :
- Algorithms, Bayes Theorem, Computer Simulation, Pattern Recognition, Automated (methods), Speech Recognition Software.
MESH :
- methods : Pattern Recognition, Automated.
- Algorithms, Bayes Theorem, Computer Simulation, Speech Recognition Software.

Abstract

In many tasks in pattern recognition, such as automatic speech recognition (ASR), optical character recognition (OCR), part-of-speech (POS) tagging, and other string recognition tasks, we are faced with a well-known inconsistency: The Bayes decision rule is usually used to minimize string (symbol sequence) error, whereas, in practice, we want to minimize symbol (word, character, tag, etc.) error. When comparing different recognition systems, we do indeed use symbol error rate as an evaluation measure. The topic of this work is to analyze the relation between string (i.e., 0-1) and symbol error (i.e., metric, integer valued) cost functions in the Bayes decision rule, for which fundamental analytic results are derived. Simple conditions are derived for which the Bayes decision rule with integer-valued metric cost function and with 0-1 cost gives the same decisions or leads to classes with limited cost. The corresponding conditions can be tested with complexity linear in the number of classes. The results obtained do not make any assumption w.r.t. the structure of the underlying distributions or the classification problem. Nevertheless, the general analytic results are analyzed via simulations of string recognition problems with Levenshtein (edit) distance cost function. The results support earlier findings that considerable improvements are to be expected when initial error rates are high.

DOI: 10.1109/TPAMI.2011.163
PubMed: 21844628

Affiliations:

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<front><div type="abstract" xml:lang="en">In many tasks in pattern recognition, such as automatic speech recognition (ASR), optical character recognition (OCR), part-of-speech (POS) tagging, and other string recognition tasks, we are faced with a well-known inconsistency: The Bayes decision rule is usually used to minimize string (symbol sequence) error, whereas, in practice, we want to minimize symbol (word, character, tag, etc.) error. When comparing different recognition systems, we do indeed use symbol error rate as an evaluation measure. The topic of this work is to analyze the relation between string (i.e., 0-1) and symbol error (i.e., metric, integer valued) cost functions in the Bayes decision rule, for which fundamental analytic results are derived. Simple conditions are derived for which the Bayes decision rule with integer-valued metric cost function and with 0-1 cost gives the same decisions or leads to classes with limited cost. The corresponding conditions can be tested with complexity linear in the number of classes. The results obtained do not make any assumption w.r.t. the structure of the underlying distributions or the classification problem. Nevertheless, the general analytic results are analyzed via simulations of string recognition problems with Levenshtein (edit) distance cost function. The results support earlier findings that considerable improvements are to be expected when initial error rates are high.</div>
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Does the cost function matter in Bayes decision rule?

Does the cost function matter in Bayes decision rule?

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English descriptors

Abstract

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