In this paper, we present two learning mechanisms for arti cial neural networks (ANNs) that can be applied to solve classi cation problems with binary outputs. These mechanisms are used to reduce the number of hidden units of an ANN when trained by the cascade-correlation learning algorithm (CAS). Since CAS adds hidden units incrementally as learning proceeds, it is di cult to predict the number of hidden units required when convergence is reached. Further, learning must be restarted when the number of hidden units is larger than expected. Our key idea in this paper is to provide alternatives in the learning process and to select the best alternative dynamically based on run-time information obtained. Mixed-mode learning (MM), our rst algorithm, provides alternative output matrices so that learning is extended to nd one of the many one-to-many mappings instead of nding a unique one-to-one mapping. Since the objective of learning is relaxed by this transformation, the number of learning epochs can be reduced. This in turn leads to a smaller number of hidden units required for convergence. Population-based Learning for ANNs (PLAN), our second algorithm, maintains alternative network con gurations and to select at run time promising networks to train based on error information obtained and time remaining. This dynamic scheduling avoids training possibly unpromising ANNs to completion before exploring new ones. We show the performance of these two mechanisms by applying them to solve the two-spiral problem, a two-region classi cation problem, and the Pima Indian Diabetes Diagnosis problem.
Teng and Wah developed learning mechanism by reducing the number of hidden units of a neural network when trained REF .
Automated Learning for Reducing the Configuration of a Feed-Forward Neural Network
IEEE transactions on neural networks
IEEE transactions on neural networks
Computer Science Medicine