针对增量学习模型在更新阶段的识别效果不稳定的问题,提出一种基于目标均衡度量的核增量学习方法.通过设置经验风险均值最小化的优化目标项,设计了均衡度量训练数据个数的优化目标函数,以及在增量学习训练条件下的最优求解方案;再结合基于重要性分析的新增数据有效选择策略,最终构建出了一种轻量型的增量学习分类模型.在跌倒检测公开数据集上的实验结果显示:当已有代表性方法的识别精度下滑至60％以下时,所提方法仍能保持95％以上的精度,同时模型更新的计算消耗仅为3ms.实验结果表明,所提算法在显著提高增量学习模型更新阶段识别能力稳定性的同时,大大降低了时间消耗,可有效实现云服务平台中关于可穿戴设备终端的智能应用.%In view of the problem that conventional incremental learning models may go through a way of performance degradation during the update stage,a kernelized incremental learning method was proposed based on objective equilibrium measurement.By setting the optimization term of "empirical risk minimization",an optimization objective function fulfilling the equilibrium measurement with respect to training data size was designed.The optimal solution was given under the condition of incremental learning training,and a lightweight incremental learning classification model was finally constructed based on the effective selection strategy of new data.Experimental results on a publicly available fall detection dataset show that,when the recognition accuracy of representative methods falls below 60％,the proposed method can still maintain the recognition accuracy more than 95％,while the computational consumption of the model update is only 3 milliseconds.In conclusion,the proposed method contributes to achieving a stable growth of recognition performance as well as efficiently decreasing the time consumptions,which can effectively realize wearable devices based intellectual applications in the cloud service platform.
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