  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
advance FastMapSVM -- an interpretable Machine Learning framework for
classifying complex objects -- as an advantageous alternative to Neural
Networks for general classification tasks. FastMapSVM extends the applicability
of Support-Vector Machines (SVMs) to domains with complex objects by combining
the complementary strengths of FastMap and SVMs. FastMap is an efficient
linear-time algorithm that maps complex objects to points in a Euclidean space
while preserving pairwise domain-specific 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.
