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Neural Networks and related Deep Learning methods are currently at theleading edge of technologies used for classifying objects. However, theygenerally demand large amounts of time and data for model training; and theirlearned models can sometimes be difficult to interpret. In this paper, weadvance FastMapSVM -- an interpretable Machine Learning framework forclassifying complex objects -- as an advantageous alternative to NeuralNetworks for general classification tasks. FastMapSVM extends the applicabilityof Support-Vector Machines (SVMs) to domains with complex objects by combiningthe complementary strengths of FastMap and SVMs. FastMap is an efficientlinear-time algorithm that maps complex objects to points in a Euclidean spacewhile preserving pairwise domain-specific distances between them. Wedemonstrate the efficiency and effectiveness of FastMapSVM in the context ofclassifying 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 smalleramounts of time and data for model training. It also provides a perspicuousvisualization of the objects and the classification boundaries between them. Weexpect FastMapSVM to be viable for classification tasks in many otherreal-world domains.

FastMapSVM: Classifying Complex Objects Using the FastMap Algorithm and Support-Vector Machines