NDLI: Weighted Automata Sequence Kernel

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Weighted Automata Sequence Kernel

Content Provider	ACM Digital Library
Author	Ziadi, Djelloul Cherroun, Hadda Nehar, Attia Bellaouar, Slimane
Abstract	Sequence kernels are widely used for learning from sequential data. The literature includes a variety of sequence kernels. In this paper, we present a general framework to deal with sequence kernels, termed weighted automata sequence kernel. In fact, the mapping of a string s to a high dimensional feature space can be modeled by a formal power series that can be realized by a weighted automaton (WA) As representing all subsequences. The computation of the kernel between two strings K(s,t) is the behavior of the WA As,t = As ∩ At. Thus, as formulated, it can be generalized to sequence set kernel. For the kernel computation efficiency, we propose a forward lookup automata intersection technique to prevent non successful ∈-paths while evaluating the WA computations. The experiments use the Reuters-21578 collection. The results reveal that the kernel evaluation using our proposed technique is faster than that using the standard intersection. We also study the relationship between our general framework and a variety of common sequence kernels. Finally, based on our general framework, we describe the essential to create a new sequence-set kernel that can also be seen as a tree kernel.
Starting Page	48
Ending Page	55
Page Count	8
File Format	PDF
ISBN	9781450348171
DOI	10.1145/3055635.3056637
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2017-02-24
Publisher Place	New York
Access Restriction	Subscribed
Subject Keyword	Formal power series Weighted automata Sequence-set kernel Sequence kernel
Content Type	Text
Resource Type	Article

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