Boolean Feature Discovery in Empirical Learning
Machine Learning
Neural network learning and expert systems
Neural network learning and expert systems
The cascade-correlation learning architecture
Advances in neural information processing systems 2
Extracting Refined Rules from Knowledge-Based Neural Networks
Machine Learning
Symbolic knowledge extraction from trained neural networks: a sound approach
Artificial Intelligence
Data Structures: Theory and Practice
Data Structures: Theory and Practice
FERNN: An Algorithm for Fast Extraction of Rules fromNeural Networks
Applied Intelligence
Constructive Neural Networks
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A particular group of neural network (NN) learning algorithms known as constructive algorithms (CoNN) congregates algorithms that dynamically combine two processes: (1) the definition of the NN architecture and (2) learning. Generally both processes alternate, depending on each others performance. During training CoNN algorithms incrementally add hidden neurons and connections to the network until some stopping criterion is satisfied. This paper describes an investigation into the semantic role played by the hidden neurons added into the NN, when learning Boolean functions. Five CoNN algorithms namely Tower, Pyramid, Tiling, Perceptron-Cascade and Shift are examined in that respect. Results show that hidden neurons represent Boolean sub-expressions whose combination represents a disjunction of prime implicants.