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Exploiting remote learners in Internet environment with agents

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Abstract

Data in the Internet are scattered on different sites indeliberately, and accumulated and updated frequently but not synchronously. It is infeasible to collect all the data together to train a global learner for prediction; even exchanging learners trained on different sites is costly. In this paper, aggregative-learning is proposed. In this paradigm, every site maintains a local learner trained from its own data. Upon receiving a request for prediction, an aggregative-learner at a local site activates and sends out many mobile agents taking the request to potential remote learners. The prediction of the aggregative-learner is made by combining the local prediction and the responses brought back by the agents. Theoretical analysis and simulation experiments show the superiority of the proposed method.

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Authors and Affiliations

  1. National Key Laboratory for Novel Software Technology, Nanjing University, Nanjing, 210093, China

    Ming Li, Wei Wang & ZhiHua Zhou

Authors
  1. Ming Li
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  2. Wei Wang
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  3. ZhiHua Zhou
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Correspondence to ZhiHua Zhou.

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Li, M., Wang, W. & Zhou, Z. Exploiting remote learners in Internet environment with agents. Sci. China Ser. F-Inf. Sci. 53, 64–76 (2010). https://doi.org/10.1007/s11432-010-0011-2

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  • DOI: https://doi.org/10.1007/s11432-010-0011-2

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