Automated Knowledge Acquisition

This tutorial provides clear explanations of techniques for automated knowledge acquisition. Covers topics such as induction algorithms using decision trees; induction algorithms using progressive rule generation; sub-symbolic learning methods, artificial neural networks; other machine learning paradigms; theoretical considerations; the extraction of rules and concepts using a single-layered Hebbian neural network; BRAINNE - automated knowledge acquisition using multi-layered neural network; and BRAINNE in the real world. For computer professionals who wish to gain a good understanding of automated knowledge acquisition techniques.

This tutorial provides clear explanations of techniques for automated knowledge acquisition. The techniques covered include: decision tree methods, progressive rule generation, explanation-based learning, artificial neural networks, and genetic algorithm approaches. The book is suitable for both advanced undergraduate and graduate students and computer professionals.