论文标题
Super-K：基于Voronoi Tessellations的分段线性分类器
Super-k: A Piecewise Linear Classifier Based on Voronoi Tessellations
论文作者
论文摘要
Voronoi Tessellations用于将欧几里得空间划分为多面体区域，该区域称为Voronoi细胞。将Voronoi细胞标记为类信息，我们可以将任何分类问题映射到Voronoi tessellation中。这样，分类问题会变成仅查找封闭的Voronoi单元的查询。为了完成此任务，我们开发了一种新的算法，该算法生成了标记的voronoi tessellation，将训练数据分配到多面体区域并获得阶级间边界作为间接结果。它被称为监督的K-素或简称Super-K。我们将Super-K作为基本的新算法介绍，并开放了新的算法家族的可能性。在本文中，通过在某些数据集上的比较显示，Super-K算法具有提供众所周知的SVM算法家族的可比性，其复杂性较小。
Voronoi tessellations are used to partition the Euclidean space into polyhedral regions, which are called Voronoi cells. Labeling the Voronoi cells with the class information, we can map any classification problem into a Voronoi tessellation. In this way, the classification problem changes into a query of just finding the enclosing Voronoi cell. In order to accomplish this task, we have developed a new algorithm which generates a labeled Voronoi tessellation that partitions the training data into polyhedral regions and obtains interclass boundaries as an indirect result. It is called Supervised k-Voxels or in short Super-k. We are introducing Super-k as a foundational new algorithm and opening the possibility of a new family of algorithms. In this paper, it is shown via comparisons on certain datasets that the Super-k algorithm has the potential of providing comparable performance of the well-known SVM family of algorithms with less complexity.
