Perceptron learning in the domain of graphs
Brijnesh Johannes Jain, Fritz Wysotzki · 2004
We develop a new mathematical framework, which embeds weighted graphs into quasi metric spaces. This concept establishes a theoretical basis to apply neural learning machines for structured data. To exemplarily illustrate the applicability of metric graph spaces, we propose and analyze a perceptron learning algorithm for graphs in its primal and dual form.