Neural Networks and related Deep Learning methods are currently at the leading edge of technologies used for classifying objects. However, they generally demand large amounts of time and data for model training; and their learned models can sometimes be difficult to interpret. In this paper, we re-introduce FastMapSVM, an interpretable Machine Learning framework for classifying complex objects. FastMapSVM combines the strengths of FastMap and Support-Vector Machines. FastMap is an efficient linear-time algorithm that maps complex objects to points in a Euclidean space, while preserving pairwise non-Euclidean distances between them. We demonstrate the efficiency and effectiveness of FastMapSVM in the context of classifying seismograms. We show that its performance, in terms of precision, recall, and accuracy, is comparable to that of other state-of-the-art methods. However, compared to other methods, FastMapSVM uses significantly smaller amounts of time and data for model training. It also provides a perspicuous visualization of the objects and the classification boundaries between them. We expect FastMapSVM to be viable for classification tasks in many other real-world domains.

翻译：目前,神经网络和相关的深层学习方法是用于对物体进行分类的技术的最前沿。但是,它们一般需要大量的时间和数据进行模型培训;它们所学的模型有时很难解释。在本文中,我们重新推出可解释的复杂物体分类的机器学习框架快速马普SVM。快速马普SVM结合了快速地图和辅助-助控机器的长处。快速马普是一种高效的线性时间算法,它将复杂的物体映射到欧几里德空间的点,同时保持它们之间的对称非欧几里德距离。我们展示了快速马普SVM在对地震图进行分类方面的效率和效力。我们显示,从精确性、回顾性和准确性来看,其性能与其他最先进的方法相似。但与其他方法相比,快速马普SVM在模型培训中使用的时间和数据要小得多。它也为天体物体和天体之间的分类提供了清晰可见度。我们期望快速马普VM在现实领域对其它领域进行可行的分类。