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Fundamental Laws of Binary Classification
Finding discriminant functions of minimum risk binary classification systems is a novel geometric locus problem -- which requires solving a system of fundamental locus equations of binary classification -- subject to deep-seated statistical laws.
Design of Data-Driven Mathematical Laws for Optimal Statistical Classification Systems
I will devise three systems of data-driven, locus equations that generate optimal, statistical classification systems.
Resolving the Geometric Locus Dilemma for Support Vector Learning Machines
It is shown that learning principal eigenaxes of linear decision boundaries involves finding a point of statistical equilibrium for which eigenenergies of principal eigenaxis components are symmetrically balanced with each other.
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