Linear classifier design in the weight space

Chulhee Lee, Seongyoun Woo

Research output: Contribution to journalArticlepeer-review

3 Citations (Scopus)

Abstract

In this paper, we propose a linear classifier design method in the weight space. A linear classifier is completely determined by a weight vector. To design a linear classifier is equivalent to finding a weight vector. When there are a number of training samples, each training sample represents a plane in the weight space. On one side of the plane, the training sample is correctly classified while it is incorrectly classified on the other side. Thus, finding the optimal linear classifier can be formulated as finding a subspace that provides the best classification accuracy. In this paper, we have developed an algorithm to find such optimal subspaces in the weight space. Although we could not find the optimal linear classifier due to prohibitive computational complexity, the proposed design method produced noticeable improvements in some cases. Experimental results show that the proposed linear classifier performed better than or equivalent to existing linear classifiers.

Original languageEnglish
Pages (from-to)210-222
Number of pages13
JournalPattern Recognition
Volume88
DOIs
Publication statusPublished - 2019 Apr

Bibliographical note

Funding Information:
This work was supported by a National Research Foundation of Korea ( NRF ) grant funded by the Korean government (MSIP) (No. 2015R1A2A2A01006421 ).

Funding Information:
This work was supported by a National Research Foundation of Korea (NRF) grant funded by the Korean government (MSIP) (No. 2015R1A2A2A01006421).

Publisher Copyright:
© 2018 Elsevier Ltd

All Science Journal Classification (ASJC) codes

  • Software
  • Signal Processing
  • Computer Vision and Pattern Recognition
  • Artificial Intelligence

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