  We apply the support vector machine (SVM) algorithm to derive a set of
entanglement witnesses (EW) to identify entanglement patterns in families of
four-qubit states. The effectiveness of SVM for practical EW implementations
stems from the coarse-grained description of families of equivalent entangled
quantum states. The equivalence criteria in our work is based on the stochastic
local operations and classical communication (SLOCC) classification and the
description of the four-qubit entangled Werner states. We numerically verify
that the SVM approach provides an effective tool to address the entanglement
witness problem when the coarse-grained description of a given family state is
available. We also discuss and demonstrate the efficiency of nonlinear kernel
SVM methods as applied to four-qubit entangled state classification.
