10.5281/zenodo.1188188
Sarunya Kanjanawattana
Graduate School, Shibaura Institute of Technology, Tokyo, Japan
Masaomi Kimura
Information Science and Engineering, Shibaura Institute of Technology, Tokyo, Japan
Brain Journal-Annsvm: A Novel Method For Graph-Type Classification By Utilization Of Fourier Transformation, Wavelet Transformation, And Hough Transformation-Figure 6. Results From Annsvn That Used Wl And Ht
Zenodo
2017
Graph-type classification
wavelet transformation
dimensionality reduction
artificial neural networks
2017-07-27
en
Figure
https://zenodo.org/record/1188189
10.5281/zenodo.1188189
Creative Commons Attribution 4.0
Open Access
<p>To identify which features of data influentially impacted data separability, we conducted experiments for ANNSVM with WL and HT (i.e., Figure 6). The WL contained only wavelet coefficients, whereas HT included only results of the Hough transformation. We found that, again, results obtained via the linear kernel were not significant; however, using the RBF kernel, accuracy for WL was higher than that of HT, indicating that wavelet coefficients provide influential features that make data separable. </p>
https://www.edusoft.ro/brain/index.php/brain/article/view/685/763