Recursive total error rate minimization

Se In Jang, Geok Choo Tan, Kar Ann Toh

Research output: Chapter in Book/Report/Conference proceedingConference contribution


In learning algorithm design, a direct optimization of a cost function which fits the goal is naturally desired. Particularly in recursive learning, a direct formulation for total-error-rate (TER) minimization is much desired for online classification applications. However, due to a nonlinear counting step in the classification formulation, an exact solution to minimize TER recursively is yet to be established. In this paper, we propose an exact recursive formulation for TER minimization. Based on empirical evaluations using benchmark data sets, we show that the proposed recursive classification algorithm preserves the performance of the batch mode TER while easing the computational memory load by sample based accumulation.

Original languageEnglish
Title of host publicationProceedings of the 2016 IEEE Region 10 Conference, TENCON 2016
PublisherInstitute of Electrical and Electronics Engineers Inc.
Number of pages4
ISBN (Electronic)9781509025961
Publication statusPublished - 2017 Feb 8
Event2016 IEEE Region 10 Conference, TENCON 2016 - Singapore, Singapore
Duration: 2016 Nov 222016 Nov 25

Publication series

NameIEEE Region 10 Annual International Conference, Proceedings/TENCON
ISSN (Print)2159-3442
ISSN (Electronic)2159-3450


Other2016 IEEE Region 10 Conference, TENCON 2016

Bibliographical note

Publisher Copyright:
© 2016 IEEE.

All Science Journal Classification (ASJC) codes

  • Computer Science Applications
  • Electrical and Electronic Engineering


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