  Machine learning is capable of discriminating phases of matter, and finding
associated phase transitions, directly from large data sets of raw state
configurations. In the context of condensed matter physics, most progress in
the field of supervised learning has come from employing neural networks as
classifiers. Although very powerful, such algorithms suffer from a lack of
interpretability, which is usually desired in scientific applications in order
to associate learned features with physical phenomena. In this paper, we
explore support vector machines (SVMs) which are a class of supervised kernel
methods that provide interpretable decision functions. We find that SVMs can
learn the mathematical form of physical discriminators, such as order
parameters and Hamiltonian constraints, for a set of two-dimensional spin
models: the ferromagnetic Ising model, a conserved-order-parameter Ising model,
and the Ising gauge theory. The ability of SVMs to provide interpretable
classification highlights their potential for automating feature detection in
both synthetic and experimental data sets for condensed matter and other
many-body systems.
