Title: Transductive Boltzmann Machines,
Abstract: We present transductive Boltzmann machines (TBMs), which firstly achieve
transductive learning of the Gibbs distribution. While exact learning of the
Gibbs distribution is impossible by the family of existing Boltzmann machines
due to combinatorial explosion of the sample space, TBMs overcome the problem
by adaptively constructing the minimum required sample space from data to avoid
unnecessary generalization. We theoretically provide bias-variance
decomposition of the KL divergence in TBMs to analyze its learnability, and
empirically demonstrate that TBMs are superior to the fully visible Boltzmann
machines and popularly used restricted Boltzmann machines in terms of
efficiency and effectiveness.