Recruitment learning of boolean functions in sparse random networks.
This work presents a new class of neural network models constrained by biological levels of sparsity and weight-precision, and employing only local weight updates. Concept learning is accomplished through the rapid recruitment of existing network knowledge - complex knowledge being realised as a combination of existing basis concepts. Prior network knowledge is here obtained through the random generation of feedforward networks, with the resulting concept library tailored through distributional bias to suit a particular target class. Learning is exclusively local - through supervised Hebbian and Winnow updates - avoiding the necessity for backpropagation of error and allowing remarkably rapid learning. The approach is demonstrated upon concepts of varying difficulty, culminating in the well-known Monks and LED benchmark problems.
Hogan
J M
JM
Faculty of Information Technology, Queensland University of Technology, GPO Box 2434, Brisbane, 4001, Australia. j.hogan@qut.edu.au
Diederich
J
J
D000465:Algorithms; D001185:Artificial Intelligence; D003198:Computer Simulation; D003210:Concept Formation; D005246:Feedback; D017143:Fuzzy Logic; D007858:Learning; D008954:Models, Biological; D016571:Neural Networks, Computer; D011897:Random Allocation