| Btw, if you have relational data and a few good people with strong computer science backgrounds rather than statisticians or mathematicians, have a look at Inductive Logic Programming. ILP is a set of machine learning techniques that learn logic programs from logic programs. The sample efficiency is on a class of its own and it generalises robustly from very little data[1]. I study ILP algorithms for my PhD. My research group has recently developed a new technique, Meta Interpretive Learning. Its canonical implementation is Metagol: https://github.com/metagol/metagol Please feel free to email me if you need more details. My address is in my profile. ___________________ [1] As a source of this claim I always quote this DeepMind paper where Metagol is compared to the authors' own system (which is itself an ILP system, but using a deep neural net): https://arxiv.org/abs/1711.04574 ILP has a number of appealing features. First, the learned program is an
explicit symbolic structure that can be inspected, understood, and verified.
Second, ILP systems tend to be impressively data-efficient, able to generalise
well from a small handful of examples. The reason for this data-efficiency is
that ILP imposes a strong language bias on the sorts of programs that can be
learned: a short general program will be preferred to a program consisting of
a large number of special-case ad-hoc rules that happen to cover the
training data. Third, ILP systems support continual and transfer learning. The
program learned in one training session, being declarative and free of
side-effects, can be copied and pasted into the knowledge base before the next
training session, providing an economical way of storing learned knowledge. |