Incremental learning from stream data
Document Type
Article
Date of Original Version
12-1-2011
Abstract
Recent years have witnessed an incredibly increasing interest in the topic of incremental learning. Unlike conventional machine learning situations, data flow targeted by incremental learning becomes available continuously over time. Accordingly, it is desirable to be able to abandon the traditional assumption of the availability of representative training data during the training period to develop decision boundaries. Under scenarios of continuous data flow, the challenge is how to transform the vast amount of stream raw data into information and knowledge representation, and accumulate experience over time to support future decision-making process. In this paper, we propose a general adaptive incremental learning framework named ADAIN that is capable of learning from continuous raw data, accumulating experience over time, and using such knowledge to improve future learning and prediction performance. Detailed system level architecture and design strategies are presented in this paper. Simulation results over several real-world data sets are used to validate the effectiveness of this method. © 2011 IEEE.
Publication Title, e.g., Journal
IEEE Transactions on Neural Networks
Volume
22
Issue
12 PART 1
Citation/Publisher Attribution
He, Haibo, Sheng Chen, Kang Li, and Xin Xu. "Incremental learning from stream data." IEEE Transactions on Neural Networks 22, 12 PART 1 (2011): 1901-1914. doi: 10.1109/TNN.2011.2171713.