Superiority Demonstration of Variance-Considered Machines by Comparing Error Rate with Support Vector Machines
- Authors
- Yeom, Hong-Gi; Park, Seung-Min; Park, Junheong; Sim, Kwee-Bo
- Issue Date
- Jun-2011
- Publisher
- INST CONTROL ROBOTICS & SYSTEMS, KOREAN INST ELECTRICAL ENGINEERS
- Keywords
- Classification algorithm; optimal hyperplane; support vector machine; variance-considered machine
- Citation
- INTERNATIONAL JOURNAL OF CONTROL AUTOMATION AND SYSTEMS, v.9, no.3, pp 595 - 600
- Pages
- 6
- Journal Title
- INTERNATIONAL JOURNAL OF CONTROL AUTOMATION AND SYSTEMS
- Volume
- 9
- Number
- 3
- Start Page
- 595
- End Page
- 600
- URI
- https://scholarworks.bwise.kr/cau/handle/2019.sw.cau/21500
- DOI
- 10.1007/s12555-011-0321-1
- ISSN
- 1598-6446
2005-4092
- Abstract
- To improve the performance of classification algorithms, we proposed a new variance-considered machine (VCM) classification algorithm in a previous study. The study showed theoretically that VCMs have lower error probabilities than SVMs. The purpose of this paper is to experimentally demonstrate the superiority of VCMs. Therefore, we verified our proposal with several case experiments using data following a Gaussian distribution with different variances and prior probabilities. To estimate performance, the experiment for each case was executed 1000 times and the error rates were averaged for accuracy. The data of each experiment have different distances between means of data, and different ratios between training data and testing data. Thus, we proved that the error rate of VCMs is lower than the error rate of SVMs, although their performances were not similar in each case. Consequently, we expect that VCMs will be applied to a variety fields.
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Collections - College of ICT Engineering > School of Electrical and Electronics Engineering > 1. Journal Articles
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