Kernel-based regularized risk minimizers, also called support vector machines (SVMs), are known to possess many desirable properties but suffer from their super-linear computational requirements when dealing with large data sets. This problem can be tackled by using localized SVMs instead, which also offer the additional advantage of being able to apply different hyperparameters to different regions of the input space. In this paper, localized SVMs are analyzed with regards to their consistency. It is proven that they inherit $L_p$- as well as risk consistency from global SVMs under very weak conditions and even if the regions underlying the localized SVMs are allowed to change as the size of the training data set increases.

翻译：基于核的正则化风险最小化器，即支持向量机（SVM），虽具备诸多优良特性，但在处理大规模数据集时面临超线性计算需求的问题。局部化SVM可有效解决该难题，并额外具有能为输入空间不同区域应用不同超参数的优点。本文对局部化SVM的一致性进行了分析。论证表明，在极弱条件下，即便允许局部化SVM所依据的区域随训练数据集规模增加而变化，局部化SVM仍能从全局SVM继承Lp一致性及风险一致性。