Transductive Boltzmann Machines

Autor:	Sugiyama, Mahito, Tsuda, Koji, Nakahara, Hiroyuki
Rok vydání:	2018
Předmět:	Statistics - Machine Learning Computer Science - Learning
Druh dokumentu:	Working Paper
Popis:	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. Comment: 10 pages, 1 figure, 2 tables
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/1805.07938 Zobrazit plný text záznamu View this record from Arxiv