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• W1635791370 abstract "Online learning is very important for processing sequential data and helps alleviate the computation burden on large scale data as well. Especially, one-pass online learning is to predict a new coming sample׳s label and update the model based on the prediction, where each coming sample is used only once and never stored. So far, existing one-pass online learning methods are globally modeled and do not take the local structure of the data distribution into consideration, which is a significant factor of handling the nonlinear data separation case. In this work, we propose a local online learning (LOL) method, a multiple hyperplane Passive Aggressive algorithm integrated with online clustering, so that all local hyperplanes are learned jointly and working cooperatively. This is achieved by formulating a common component as information traffic among multiple hyperplanes in LOL. A joint optimization algorithm is proposed and theoretical analysis on the cumulative error is also provided. Extensive experiments on 11 datasets show that LOL can learn a nonlinear decision boundary, overall achieving notably better performance without using any kernel modeling and second order modeling." @default.
• W1635791370 created "2016-06-24" @default.
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• W1635791370 date "2016-03-01" @default.
• W1635791370 modified "2023-10-17" @default.
• W1635791370 title "One-pass online learning: A local approach" @default.
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• W1635791370 doi "https://doi.org/10.1016/j.patcog.2015.09.003" @default.
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