Support Vector Machines versus Artificial Neural Networks for Wood Dielectric Loss Factor Estimation

Lazaros Iliadis 1 Stavros Tachos 2 Stavros Avramidis 3 Shawn Mansfield 3
1 DUTH - Democritos University of Thrace
2 Aristotle University of Thessaloniki
3 UBC - University of British Columbia
Abstract : This research effort aims in the estimation of Wood Loss Factor by employing Support Vector Machines. For this purpose experimental data for two different wood species were used. The estimation of the dielectric properties of wood was done by using various Kernel algorithms as a function of both ambient electro-thermal conditions applied during drying of wood and basic wood chemistry. Actually the best fit neural models that were developed in a previous effort of our research team were compared to the Kernels’ approaches in order to determine the optimal ones.
Keywords : Wood Loss Factor Gaussian Support Vector Machines Polynomial Support Vector Machines Artificial Neural Networks
Type de document :
Communication dans un congrès
Lazaros Iliadis; Chrisina Jayne. 12th Engineering Applications of Neural Networks (EANN 2011) and 7th Artificial Intelligence Applications and Innovations (AIAI), Sep 2011, Corfu, Greece. Springer, IFIP Advances in Information and Communication Technology, AICT-363 (Part I), pp.140-149, 2011, Engineering Applications of Neural Networks. 〈10.1007/978-3-642-23957-1_16〉
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Lazaros Iliadis, Stavros Tachos, Stavros Avramidis, Shawn Mansfield. Support Vector Machines versus Artificial Neural Networks for Wood Dielectric Loss Factor Estimation. Lazaros Iliadis; Chrisina Jayne. 12th Engineering Applications of Neural Networks (EANN 2011) and 7th Artificial Intelligence Applications and Innovations (AIAI), Sep 2011, Corfu, Greece. Springer, IFIP Advances in Information and Communication Technology, AICT-363 (Part I), pp.140-149, 2011, Engineering Applications of Neural Networks. 〈10.1007/978-3-642-23957-1_16〉. 〈hal-01571343〉

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