
TL;DR
This survey comprehensively reviews rational kernels, a sequence-based data representation method that enhances statistical learning by capturing sequence information without fixed-length vector conversion.
Contribution
It provides an extensive overview of the theory, extensions, applications, and scalability of rational kernels, highlighting their practical and theoretical development.
Findings
Rational kernels effectively represent sequence data for kernel-based learning.
The framework has been extended and applied across various domains.
Practical aspects and scalability of rational kernels have been thoroughly studied.
Abstract
Many kinds of data are naturally amenable to being treated as sequences. An example is text data, where a text may be seen as a sequence of words. Another example is clickstream data, where a data instance is a sequence of clicks made by a visitor to a website. This is also common for data originating in the domains of speech processing and computational biology. Using such data with statistical learning techniques can often prove to be cumbersome since most of them only allow fixed-length feature vectors as input. In casting the data to fixed-length feature vectors to suit these techniques, we lose the convenience, and possibly information, a good sequence-based representation can offer. The framework of rational kernels partly addresses this problem by providing an elegant representation for sequences, for algorithms that use kernel functions. In this report, we take a comprehensive…
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Taxonomy
TopicsAlgorithms and Data Compression · Machine Learning and Algorithms · semigroups and automata theory
