The recently proposed Minimal Complexity Machine (MCM) finds a hyperplane
classifier by minimizing an exact bound on the Vapnik-Chervonenkis (VC)
dimension. The VC dimension measures the capacity of a learning machine, and a
smaller VC dimension leads to improved generalization...
On many benchmark
datasets, the MCM generalizes better than SVMs and uses far fewer support
vectors than the number used by SVMs. In this paper, we describe a neural
network based on a linear dynamical system, that converges to the MCM solution. The proposed MCM dynamical system is conducive to an analogue circuit
implementation on a chip or simulation using Ordinary Differential Equation
(ODE) solvers. Numerical experiments on benchmark datasets from the UCI
repository show that the proposed approach is scalable and accurate, as we
obtain improved accuracies and fewer number of support vectors (upto 74.3%
reduction) with the MCM dynamical system.