Abstract
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.
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URL
http://arxiv.org/abs/1802.06382