A smart algorithm for incremental learning
E.H.-C. Wang, Anthony Kuh · 2003
Incremental learning algorithms not only adjust interconnection weights, but also change the network architecture by adding hidden nodes at the network. The capabilities of these incremental learning algorithms are examined. Four different incremental learning algorithms have been simulated for a variety of learning tasks. To improve the performance of the incremental learning algorithms, a new perceptron learning algorithm is proposed, the smart algorithm, to find the near-optimal set of weights at each node. The simulation results show that the smart algorithm improves the performance of these incremental learning algorithms. Among the four algorithms examined, the global algorithm performed the best.>