Optimizations of the Combinatorial Neural Model

Fabio Ghignatti BeckenkampWolfgang PreeMiguel Artur Feldens

In this paper we present significant optimizations of the so-called Combinatorial Neural Model (CNM). CNM is a hybrid (neural/symbolic) model that has been used in areas such as expert system development and data mining. The paper first explains the CNM architecture and goes on to present CNM optimizations together with empiric results. The most important optimization aims at taming combinatorial explosion, which is the main problem inherent to this model.

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