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Online kernel classification with adjustable bandwidth using control-based learning approach
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DOI：10.1016/j.patcog.2020.107566.png)
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Abstract
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In this paper, a novel control-based kernel learning approach is proposed for inferring online binary classification tasks. Following a carefully designed alternating optimization scheme, the learning problems are transformed into two optimal feedback control problems for a series of linear, controllable systems. Model parameters including weights and kernel bandwidth can be efficiently updated by solving the control problems. These consequently lead to our control-based adaptive online kernel classification algorithm (CAOKC). The bandwidth, although nonlinear in our model, can still be updated accurately after linearization. Thus, compared with the existing benchmark algorithms with fixed kernels, the CAOKC algorithm is able to achieve a more adaptive, robust classification performance with better prediction accuracy by regarding the bandwidth as an adjustable parameter. The results presented in this paper also demonstrate how optimal control can provide novel insights and be an effective approach for addressing various learning tasks. Numerical results on benchmark synthetic and realistic datasets are provided to illustrate our method. (C) 2020 Elsevier Ltd. All rights reserved.
Keywords:
Online classification
Kernel learning
Adaptive learning
Adjustable bandwidth
Control-based approach
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