Support vector machine (SVM) and other kernel techniques represent a family of powerful statistical classification methods with high accuracy and broad applicability. Because they use all or a significant portion of the training data, however, they can be slow, especially for large problems. Piecewise linear classifiers are similarly versatile, yet have the additional advantages of simplicity, ease of interpretation and, if the number of component linear classifiers is not too large, speed. Here we show how a simple, piecewise linear classifier can be trained from a kernel-based classifier in order to improve the classification speed. The method works by finding the root of the difference in conditional probabilities between pairs of opposite classes to build up a representation of the decision boundary. When tested on 17 different datasets, it succeeded in improving the classification speed of a SVM for 12 of them by up to two orders of magnitude. Of these, two were less accurate than a simple, linear classifier. The method is best suited to problems with continuum features data and smooth probability functions. Because the component linear classifiers are built up individually from an existing classifier, rather than through a simultaneous optimization procedure, the classifier is also fast to train.

中文翻译：

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通过边界映射加速内核分类器

支持向量机（SVM）和其他内核技术代表了一系列功能强大的统计分类方法，具有很高的准确性和广泛的适用性。但是，因为它们使用了全部或很大一部分训练数据，所以它们可能会很慢，尤其是对于大问题。分段线性分类器具有类似的通用性，但还具有简单，易于解释的附加优点，并且如果组成线性分类器的数量不太大，则具有速度优势。在这里，我们展示了如何从基于内核的分类器中训练简单的分段线性分类器，以提高分类速度。该方法通过找到成对的相反类之间的条件概率差异的根来建立决策边界的表示而起作用。在17个不同的数据集上进行测试时，它成功地将其中的12个支持向量机的分类速度提高了两个数量级。其中，有两个不如简单的线性分类器准确。该方法最适合于具有连续特征数据和平滑概率函数的问题。由于组件线性分类器是从现有分类器中单独构建的，而不是通过同步优化过程构建的，因此分类器的训练速度也很快。