Extracting rules from neural networks by pruning and hidden-unit splitting
Neural Computation
Rule-extraction by backpropagation of polyhedra
Neural Networks
Symbolic Rule Extraction from the DIMLP Neural Network
Hybrid Neural Systems, revised papers from a workshop
Extracting Provably Correct Rules from Artificial Neural Networks
Extracting Provably Correct Rules from Artificial Neural Networks
PRIB'07 Proceedings of the 2nd IAPR international conference on Pattern recognition in bioinformatics
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An algorithm to extract representations from feed-forward threshold networks is outlined. The representation is based on polytopic decision regions in the input space - and is exact not an approximation. Using this exact representation we explore scope questions, such as when and where do networks form artifacts, or what can we tell about network generalization from its representation. The exact nature of the algorithm also lends itself to theoretical questions about representation extraction in general, such as what is the relationship between factors such as input dimensionality, number of hidden units, number of hidden layers, and how the network output is interpreted to the potential complexity of the network's function.