2012 IEEE 24th International Conference on Tools with Artificial Intelligence (2009)
Newark, New Jersey
Nov. 2, 2009 to Nov. 4, 2009
DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/ICTAI.2009.65
This paper describes an attempt to devise a knowledge discovery model that is inspired from the two theoretical frameworks of selectionism and constructivism in human cognitive learning. The "selectionist" nature of human decision making indicates the use of an evolutionary paradigm for composing rudimentary neural network units, while the "constructivist" component takes the form of neural weight training during the learning process. We explore the possibility of amalgamating these two ideas into a neural learning system for the discovery of meaningful rules in the context of pattern discovery in data.
Henry Wai Kit Chia, Chew Lim Tan, Sam Y Sung, "Probabilistic Neural Logic Network Learning: Taking Cues from Neuro-Cognitive Processes", 2012 IEEE 24th International Conference on Tools with Artificial Intelligence, vol. 00, no. , pp. 698-702, 2009, doi:10.1109/ICTAI.2009.65