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Potential Functions for Signals and Symbolic Sequences

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Book cover Braverman Readings in Machine Learning. Key Ideas from Inception to Current State

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 11100))

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Abstract

This paper contains a comprehensive survey of possible ways for potential functions design on sets of signals and symbolic sequences. Significant emphasis is placed on a generalized probabilistic approach to construction of potential functions. This approach covers both vector signals and symbolic sequences at once and leads to a large family of potential functions based on the notion of a random transformation of signals and sequences, which can underlie, in particular, probabilistic models of evolution of biomolecular sequences. We show that some specific choice of the sequence random transformation allows to obtain such important particular cases as Global Alignment Kernel and Local Alignment Kernel. The second part of the paper addresses the multi-kernel situation, which is extremely actual, in particular, due to the necessity to combine information from different sources. A generalized probabilistic featureless SVM-based approach to combining different data sources via supervised selective kernel fusion was proposed in our previous papers. In this paper we demonstrate significant qualitative advantages of the proposed approach over other methods of kernel fusion on example of membrane protein prediction.

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Acknowledgements

The research is carried out using the equipment of the shared research facilities of HPC computing resources at Lomonosov Moscow State University.

The results of the research project are published with the financial support of Tula State University within the framework of the scientific project № 2017-63PUBL.

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Authors and Affiliations

  1. Tula State University, Tula, 300012, Lenin Ave. 92, Russia

    Valentina Sulimova

  2. Computing Center of the Russian Academy of Sciences, Moscow, 119333, Vavilov St. 40, Russia

    Vadim Mottl

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  1. Valentina Sulimova
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  2. Vadim Mottl
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Correspondence to Valentina Sulimova .

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Editors and Affiliations

  1. West Newton, MA, USA

    Lev Rozonoer

  2. National Research University Higher School of Economics, Moscow, Russia

    Boris Mirkin

  3. Rutgers University, Piscataway, NJ, USA

    Ilya Muchnik

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Sulimova, V., Mottl, V. (2018). Potential Functions for Signals and Symbolic Sequences. In: Rozonoer, L., Mirkin, B., Muchnik, I. (eds) Braverman Readings in Machine Learning. Key Ideas from Inception to Current State. Lecture Notes in Computer Science(), vol 11100. Springer, Cham. https://doi.org/10.1007/978-3-319-99492-5_1

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  • DOI: https://doi.org/10.1007/978-3-319-99492-5_1

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