| Incremental Learning from Stream Data. | |
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MedLine Citation:
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PMID: 22057060 Owner: NLM Status: Publisher |
Abstract/OtherAbstract:
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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. |
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Authors:
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Haibo He; Sheng Chen; Kang Li; Xin Xu |
Publication Detail:
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Type: JOURNAL ARTICLE Date: 2011-10-31 |
Journal Detail:
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Title: IEEE transactions on neural networks / a publication of the IEEE Neural Networks Council Volume: - ISSN: 1941-0093 ISO Abbreviation: - Publication Date: 2011 Oct |
Date Detail:
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Created Date: 2011-11-7 Completed Date: - Revised Date: - |
Medline Journal Info:
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Nlm Unique ID: 101211035 Medline TA: IEEE Trans Neural Netw Country: - |
Other Details:
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Languages: ENG Pagination: - Citation Subset: - |
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From MEDLINE®/PubMed®, a database of the U.S. National Library of Medicine
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