Kernel methods provide a powerful framework for non parametric learning. They are based on kernel functions and allow learning in a rich functional space while applying linear statistical learning tools, such as Ridge Regression or Support Vector Machines. However, standard kernel methods suffer from a quadratic time and memory complexity in the number of data points and thus have limited applications in large-scale learning. In this paper, we propose Snacks, a new large-scale solver for Kernel Support Vector Machines. Specifically, Snacks relies on a Nystr\"om approximation of the kernel matrix and an accelerated variant of the stochastic subgradient method. We demonstrate formally through a detailed empirical evaluation, that it competes with other SVM solvers on a variety of benchmark datasets.

翻译：核方法为非参数学习提供了强大的框架。它们基于核函数，允许在丰富的函数空间中进行学习，同时应用线性统计学习工具（如岭回归或支持向量机）。然而，标准核方法在数据点数量上存在二次时间与内存复杂度问题，因此在规模学习中的应用受到限制。本文提出Snacks，一种面向核支持向量机的新型大规模求解器。具体而言，Snacks依赖于核矩阵的Nyström近似以及随机次梯度方法的加速变体。通过详细的实证评估，我们正式证明它在多种基准数据集上与其他SVM求解器具有竞争力。