Variational Learning for Multi-layer networks of Linear Threshold Units
Proceedings of the Eighth International Workshop on Artificial Intelligence and Statistics, Morgan Kauffman :245-252, 2001.
Linear threshold units were originally proposed as models of biological neurons. They were widely studied in the context of the perceptron @Rosenblatt:book62. Due to the difficulties of finding a general algorithm for networks with hidden nodes, they never passed into general use. We derive an algorithm in the context of graphical models and show how it may be applied in multi-layer networks of linear threshold units. We demonstrate the algorithm through three well known datasets.