Learning from target knowledge approximation

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

15 Citations (Scopus)

Abstract

The classical Least Squares Error (LSE) learning method for pattern classification is to learn a classifier based on data density where the learning process (density-fitting error minimization) and the learning objective (classification error rate) do not find a good match. In this work, we propose to learn according to classification decision objectives directly. We shall work on two classification objectives namely, the Total Error Rate and the Receiver Operating Characteristics, and directly optimize the learning process according to these objectives. Using a learning model which is linear in its parameters, we propose two approximation methods to optimize these classification objectives. Our empirical results on biometrics fusion show comparable performances of the proposed methods with the widely used Support Vector Machines (SVM), with one of the approaches having a clear advantage of fast single-step solution.

Original languageEnglish
Title of host publication2006 1st IEEE Conference on Industrial Electronics and Applications
DOIs
Publication statusPublished - 2006 Dec 1
Event2006 1st IEEE Conference on Industrial Electronics and Applications, ICIEA 2006 - Singapore, Singapore
Duration: 2006 May 242006 May 26

Publication series

Name2006 1st IEEE Conference on Industrial Electronics and Applications

Other

Other2006 1st IEEE Conference on Industrial Electronics and Applications, ICIEA 2006
CountrySingapore
CitySingapore
Period06/5/2406/5/26

    Fingerprint

All Science Journal Classification (ASJC) codes

  • Electrical and Electronic Engineering
  • Industrial and Manufacturing Engineering

Cite this

Toh, K. A. (2006). Learning from target knowledge approximation. In 2006 1st IEEE Conference on Industrial Electronics and Applications [4026003] (2006 1st IEEE Conference on Industrial Electronics and Applications). https://doi.org/10.1109/ICIEA.2006.257074