Scalable Alignment Kernels via Space-Efficient Feature Maps

String kernels are attractive data analysis tools for analyzing string data. Among them, alignment kernels are known for their high prediction accuracies in string classifications We experimentally test ESP+ SFM on its ability to learn SVMs for large-scale string classifications with various massive string data, and we demonstrate the superior performance of our method with respect to prediction accuracy, scalability and computation efficiency.