论文标题
基于内核的监督学习的功能空间近似
Feature space approximation for kernel-based supervised learning
论文作者
论文摘要
我们提出了一种近似高甚至无限二维特征向量的方法，该方法在监督学习中起着重要作用。目的是减少培训数据的大小，从而导致较低的存储消耗和计算复杂性。此外，该方法可以被视为一种正则化技术，从而提高了学习目标功能的普遍性。与涉及完整培训数据集的数据驱动预测的计算相比，我们证明了显着改善。该方法应用于不同应用领域的分类和回归问题，例如图像识别，系统识别和海洋学时间序列分析。
We propose a method for the approximation of high- or even infinite-dimensional feature vectors, which play an important role in supervised learning. The goal is to reduce the size of the training data, resulting in lower storage consumption and computational complexity. Furthermore, the method can be regarded as a regularization technique, which improves the generalizability of learned target functions. We demonstrate significant improvements in comparison to the computation of data-driven predictions involving the full training data set. The method is applied to classification and regression problems from different application areas such as image recognition, system identification, and oceanographic time series analysis.
