Incremental learning from stream data

H. He, S. Chen, Kang Li, X. Xu

Research output: Contribution to journalArticlepeer-review

94 Citations (Scopus)

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.
Original languageEnglish
Article number6064897
Pages (from-to)1901-1914
Number of pages14
JournalIEEE Transactions on Neural Networks
Volume22
Issue number12
DOIs
Publication statusPublished - Dec 2011

ASJC Scopus subject areas

  • Artificial Intelligence
  • Computer Networks and Communications
  • Computer Science Applications
  • Software
  • Medicine(all)

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