  We generalize a support vector machine to a support spinor machine by using
the mathematical structure of wedge product over vector machine in order to
extend field from vector field to spinor field. The separated hyperplane is
extended to Kolmogorov space in time series data which allow us to extend a
structure of support vector machine to a support tensor machine and a support
tensor machine moduli space. Our performance test on support spinor machine is
done over one class classification of end point in physiology state of time
series data after empirical mode analysis and compared with support vector
machine test. We implement algorithm of support spinor machine by using
Holo-Hilbert amplitude modulation for fully nonlinear and nonstationary time
series data analysis.
