A multi-net local learning framework for pattern recognition

This paper proposes a general local learning framework to effectively alleviate the complexities of classifier design by means of "divide and conquer" principle and ensemble method. The learning framework consists of quantization layer and ensemble layer. After GLVQ and MLP are applied to the framework, the proposed method is tested on MNIST handwritten digit database. The obtained performance is very promising, an error rate with 0.99%, which is comparable to that of LeNet5, one of the best classifiers on this database. Further, in contrast to LeNet5, our method is especially suitable for a large-scale real-world classification problem.