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

Sarunya Kanjanawattana & Masaomi Kimura
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.
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