Cover Image

Erratum: Adaptive boosting with SVM classifier for moving vehicle classification

Link to publisher's homepage at http://www.elsevier.com/

Saved in:
Bibliographic Details
Main Authors: Norasmadi, Abdul Rahim, Pandian, Paulraj Murugesa, Prof. Dr., Abd Hamid, Adom, Prof. Dr.
Other Authors: norasmadi@unimap.edu.my
Format: Article
Language:English
Published: Elsevier Ltd 2014
Subjects:
Moving vehicle
Adaptive boosting
Support vector machine
One-third-octave
Online Access:http://dspace.unimap.edu.my:80/dspace/handle/123456789/32208
Tags: Add Tag
No Tags, Be the first to tag this record!
id my.unimap-32208
record_format dspace
spelling my.unimap-322082014-02-28T01:21:34Z Erratum: Adaptive boosting with SVM classifier for moving vehicle classification Norasmadi, Abdul Rahim Pandian, Paulraj Murugesa, Prof. Dr. Abd Hamid, Adom, Prof. Dr. norasmadi@unimap.edu.my paul@unimap.edu.my abdhamid@unimap.edu.my Moving vehicle Adaptive boosting Support vector machine One-third-octave Link to publisher's homepage at http://www.elsevier.com/ Profoundly hearing impaired community (PHIC) cannot moderate wisely an acoustic noise ema- nated from moving vehicle in outdoor. They are not able to distinguish either type or distance of moving vehicle approaching from behind. Therefore, the PHIC encounter risky situation while they are in outdoor. In this paper, a simple system has been proposed to identify the type and distance of a moving vehicle using adaptive boosting (AdaBoost) ensemble method. One-third-octave filter band approach has been used for extracting the significant features from the noise emanated by the moving vehicle. The extracted features were associated with the type and distance of the moving vehicle. A support vector machines (SVM) has been used as a weak classifer during the AdaBoost classification. The AdaBoost classification system outperforms the single classifier system in terms of classification accuracy. 2014-02-28T01:21:33Z 2014-02-28T01:21:33Z 2013 Article Procedia Engineering, vol. 53, 2013, page 728 978-162748634-7 1877-7058 http://www.sciencedirect.com/science/article/pii/S1877705813010497 http://dspace.unimap.edu.my:80/dspace/handle/123456789/32208 en Elsevier Ltd
institution Universiti Malaysia Perlis
building UniMAP Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Malaysia Perlis
content_source UniMAP Library Digital Repository
url_provider http://dspace.unimap.edu.my/
language English
topic Moving vehicle
Adaptive boosting
Support vector machine
One-third-octave
spellingShingle Moving vehicle
Adaptive boosting
Support vector machine
One-third-octave
Norasmadi, Abdul Rahim
Pandian, Paulraj Murugesa, Prof. Dr.
Abd Hamid, Adom, Prof. Dr.
Erratum: Adaptive boosting with SVM classifier for moving vehicle classification
description Link to publisher's homepage at http://www.elsevier.com/
author2 norasmadi@unimap.edu.my
author_facet norasmadi@unimap.edu.my
Norasmadi, Abdul Rahim
Pandian, Paulraj Murugesa, Prof. Dr.
Abd Hamid, Adom, Prof. Dr.
format Article
author Norasmadi, Abdul Rahim
Pandian, Paulraj Murugesa, Prof. Dr.
Abd Hamid, Adom, Prof. Dr.
author_sort Norasmadi, Abdul Rahim
title Erratum: Adaptive boosting with SVM classifier for moving vehicle classification
title_short Erratum: Adaptive boosting with SVM classifier for moving vehicle classification
title_full Erratum: Adaptive boosting with SVM classifier for moving vehicle classification
title_fullStr Erratum: Adaptive boosting with SVM classifier for moving vehicle classification
title_full_unstemmed Erratum: Adaptive boosting with SVM classifier for moving vehicle classification
title_sort erratum: adaptive boosting with svm classifier for moving vehicle classification
publisher Elsevier Ltd
publishDate 2014
url http://dspace.unimap.edu.my:80/dspace/handle/123456789/32208
_version_ 1643796831605358592
score 13.214268

Similar Items