In this paper we propose an automatic selection of the bandwidth of the recursive non-parametric estimation of the kernel classification rule function defined by the stochastic approximation algorithm, when the explanatory data are curves and the response is categorical. We established a central limit theorem for our proposed recursive estimators, the proposed recursive estimators will be very competitive to the non-recursive one in terms of estimation error but much better in terms of computational costs. The proposed estimators are used first on simulated waveform curves and then on real phoneme data.

中文翻译：

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独立功能数据的递归非参数核分类规则估计

本文提出了一种随机选择算法，当解释性数据为曲线且响应为分类响应时，自动选择由随机近似算法定义的核分类规则函数的递归非参数估计的带宽。我们为拟议的递归估计量建立了一个中心极限定理，在估计误差方面，拟议的递归估计量与非递归估计量相比非常有竞争力，但在计算成本方面要好得多。拟议的估计器首先用于模拟波形曲线，然后用于实际音素数据。