Fast learning neural nets with adaptive learning styles

There are many learning methods in artificial neural networks. Depending on the application, one learning or weight update rule may be more suitable than another, but the choice is not always clear-cut, despite some fundamental constraints, such as whether the learning is supervised or unsupervised. This paper addresses the learning style selection problem by proposing an adaptive learning style. Initially, some observations concerning the nature of adaptation and learning are discussed in the context of the underlying motivations for the research, and this paves the way for the description of an example system. The approach harnesses the complementary strengths of two forms of learning which are dynamically combined in a rapid form of adaptation that balances minimalist pattern intersection learning with Learning Vector Quantization. Both methods are unsupervised, but the balance between the two is determined by a performance feedback parameter. The result is a data-driven system that shifts between alternative solutions to pattern classification problems rapidly when performance is poor, whilst adjusting to new data slowly, and residing in the vicinity of a solution when performance is good.