On hadamard-type output coding in multiclass learning

Aijun ZHANG*, Zhi Li Wu, Chun Hung Li, Kai Tai Fang

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

10 Citations (Scopus)


The error-correcting output coding (ECOC) method reduces the multiclass learning problem into a series of binary classifiers. In this paper, we consider the dense ECOC methods, combining an economical number of base learners. Under the criteria of row separation and column diversity, we suggest the use of Hadamard matrices to design output codes and show them better than other codes of the same size. Comparative experiments based on the support vector machines are made for some real datasets from the UCI machine learning repository.

Original languageEnglish
Pages (from-to)397-404
Number of pages8
JournalLecture Notes in Computer Science
Publication statusPublished - 2004

Scopus Subject Areas

  • Theoretical Computer Science
  • Computer Science(all)

User-Defined Keywords

  • Error-correcting output codes
  • Hadamard matrix
  • Multiclass learning
  • Support vector machines


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