A Neural Network Model for Large-Scale Stream Data Learning Using Locally Sensitive Hashing

摘要

Recently, mining knowledge from stream data such as access logs of computer, commodity distribution data, sales data, and human lifelog have been attracting many attentions. As one of the techniques suitable for such an environment, active learning has been studied for a long time. In this work, we propose a fast learning technique for neural networks by introducing Locality Sensitive Hashing (LSH) and a local learning algorithm with LSH in RBF networks.