Abstract
A learning algorithm based on primary school teaching and learning is presented. The methodology is to continuously evaluate the performance of the network and to train it on the examples for which they repeatedly fail, until all the examples are correctly classified. Empirical analysis on UCI data show that the algorithm produces good training data and improves the generalization ability of the network on unseen data. The algorithm has interesting applications in data mining, model evaluations and rare objects discovery.
Author
Ninan Sajeeth Philip
Journal
PALADYN Journal of Behavioral Robotics, Vol.1, Iss.3, pp.160, 2010
Paper Publication Date
2010
Paper Type
Other