Improve Prediction with Remote Learners in Internet Environment

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 of a local site activates and sends out many mobile agents taking the request to potential remote learners. The prediction of the aggregativelearner is made by combining the local prediction and the responses brought back by the agents. Experiments show that the prediction of a local learner could be significantly improved through employing the aggregative-learning paradigm.