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International Journal of Computer Mathematics Volume 81, 2004 - Issue 5
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Original Articles

Prediction performance of support vector machines on input vector normalization methods

Daehyon KimCorrespondence[email protected]
Pages 547-554 | Received 05 Dec 2003, Published online: 12 May 2010

Citations (11)

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Read on this site (5)

Daehyon Kim. (2009) Performance Improvement in Image Processing Techniques for Traffic Data Collection. International Journal of Urban Sciences 13:2, pages 161-171.
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Qiang Wang. (2009) Underwater bottom still mine classification using robust time–frequency feature and relevance vector machine. International Journal of Computer Mathematics 86:5, pages 794-806.
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Florence Lai, Frédéric Magoulès & Fred Lherminier. (2008) Vapnik's learning theory applied to energy consumption forecasts in residential buildings. International Journal of Computer Mathematics 85:10, pages 1563-1588.
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Daehyon Kim, Seungjae Lee & Seongkil Cho. (2007) Input Vector Normalization Methods in Support Vector Machines for Automatic Incident Detection. Transportation Planning and Technology 30:6, pages 593-608.
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Daehyon Kim. (2005) Parameter Decision for Efficient Automatic Incident Detection System. International Journal of Urban Sciences 9:2, pages 78-86.
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Articles from other publishers (6)

Jun Yang, Jingyun Wang, Chen Zhao, Lingqing Wang, Xiaoming Wan, Huading Shi, Mei Lei, Tongbin Chen & Xiaoyong Liao. (2024) Identifying driving factors of soil heavy metal at the mining area scale: Methods and practice. Chemosphere 350, pages 140936.
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Daehyon Kim. (2023) Traffic Image Analysis Based on Stacked Denoising Autoencoder Neural Network. Journal of Innovation Information Technology and Application (JINITA) 5:2, pages 183-192.
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Jun Yang, Jing-yun Wang, Chen Zhao, Lingqing Wang, Xiaoming Wan, Huading Shi, Mei Lei, Tongbin Chen & Xiaoyong Liao. (2023) Identifying Driving Factors of Arsenic Distribution at the Mining Area Scale: Methods and Practice. SSRN Electronic Journal.
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Satyabrata Das, B Rajanarayan Prusty & Kishore Bingi. (2021) Review of adaptive decomposition-based data preprocessing for renewable generation rich power system applications. Journal of Renewable and Sustainable Energy 13:6.
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F.J. Mart?nez L?pez, S. Mart?nez Puertas & J.A. Torres Arriaza. (2014) Training of support vector machine with the use of multivariate normalization. Applied Soft Computing 24, pages 1105-1111.
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Gwang-Jun Kim, Dae-Hyon Kim & Yong-Kab Kim. 2011. Multimedia, Computer Graphics and Broadcasting. Multimedia, Computer Graphics and Broadcasting 234 240 .

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