CSLS: Connectionist Symbolic Learning System
AbstractThis paper presents CSLS, a symbiotic combination of inductive and neural learning. CSLS has two components, an induction algorithm to carry out inductive learning and a multi-layer perceptron (MLP) to implement neural learning. The paper outlines the operation of the components of CSLS and describes how the combined system is designed to utilise the individual strengths of inductive and neural learning to the best advantage. The paper gives the results of evaluating CSLS on the IRIS data and Breast-Cancer-Wisconsin-data classification problems. These clearly demonstrate the main benefit of the symbiotic combination: the combined system performs better than either of its components.
Scifeed alert for new publicationsNever miss any articles matching your research from any publisher
- Get alerts for new papers matching your research
- Find out the new papers from selected authors
- Updated daily for 49'000+ journals and 6000+ publishers
- Define your Scifeed now
Aksoy, M.S.; Mathkou, H. CSLS: Connectionist Symbolic Learning System. Math. Comput. Appl. 2009, 14, 177-186.
Aksoy MS, Mathkou H. CSLS: Connectionist Symbolic Learning System. Mathematical and Computational Applications. 2009; 14(3):177-186.Chicago/Turabian Style
Aksoy, Mehmet S.; Mathkou, Hassan. 2009. "CSLS: Connectionist Symbolic Learning System." Math. Comput. Appl. 14, no. 3: 177-186.