Variational Learning for Multi-Layer Networks of Linear Threshold Units
Proceedings of the Eighth International Workshop on Artificial Intelligence and Statistics, PMLR R3:168-175, 2001.
Linear threshold units (LTUs) were originally proposed as models of biological neurons. They were widely studied in the context of the perceptron (Rosenblatt, 1962). Due to the difficulties of finding a general algorithm for networks with hidden nodes, they never passed into general use. In this work we derive an algorithm in the context of probabilistic models and show how it may be applied in multi-layer networks of LTUs. We demonstrate the performance of the algorithm on three data-sets.