The Kernel Addition Training Algorithm: Faster Training for CMAC Based Neural Networks
David J. Cornforth, David Newth · Charles Sturt University Research Output (CRO) · 2001
The rapidly increasing size of databases creates a need for new algorithms to solve multi-class categorisation problems. Machine learning techniques such as neural networks have been successfully applied to this class of problems. However training times for these techniques can blow out as the size of the database increases. Some of the desirable features of algorithms for large databases are low order time complexity, training with only a single pass of the data, and accountability for class assignment decisions. We propose a new training algorithm for Cerebellar Model Articulation Controller (CMAC) based classifiers, which possesses these features. The training algorithm proposed here is based on a kernel addition method. An empirical investigation of this training method has found it to be superior to traditional techniques both in accuracy and time required to learn mappings between input vectors and class labels.