The Classification of Valid and Invalid Beats of Three-Dimensional Nystagmus Eye Movement Signals Using Machine Learning Methods

<table>Classification results with support vector machines after using the best parameter selections from Table <a href="../tab3/">3</a> for the linear, quadratic, and radial basis function (RBF) kernels with dataset variations: (1) manual selection and no cleaning of rejected nystagmus candidates, (2) manual selection and with cleaning of rejected candidates, (3) automatic selection and no cleaning, (4) automatic selection with cleaning, (5) both manual and automatic selection and no cleaning, and (6) both manual and automatic selection with cleaning.</table>

Advances in Artificial Neural Systems

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Figure 7

Figure 7: The Classification of Valid and Invalid Beats of Three-Dimensional Nystagmus Eye Movement Signals Using Machine Learning Methods 