A fast modular implementation for neural networks

Nowdays, neural networks have been widely used. In some areas, such as bioinfomatics and data mining, the problems are usually large-scaled and time-concerned. Thus, the requirement of fast learning algorithm and network architecture for large scale problems is stressed. In this paper, a novel network architecture consisting of several network modules is proposed. In this architecture, each network module is trained to learn a subset of the whole training data, and a number of neural quantizers are used to activate the corresponding network module to output. In principle, this modular network architecture (MNA) can effectively reduce the learning time and achieve the similar gerneralization performance. The results of experiments on several real-world benchmarking problems shown here to make the algorithm convincible.