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Opening the Black Box: Revealing Interpretable Sequence Motifs in Kernel-Based Learning Algorithms

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9285)

Abstract

This work is in the context of kernel-based learning algorithms for sequence data. We present a probabilistic approach to automatically extract, from the output of such string-kernel-based learning algorithms, the subsequences—or motifs—truly underlying the machine’s predictions. The proposed framework views motifs as free parameters in a probabilistic model, which is solved through a global optimization approach. In contrast to prevalent approaches, the proposed method can discover even difficult, long motifs, and could be combined with any kernel-based learning algorithm that is based on an adequate sequence kernel. We show that, by using a discriminate kernel machine such as a support vector machine, the approach can reveal discriminative motifs underlying the kernel predictor. We demonstrate the efficacy of our approach through a series of experiments on synthetic and real data, including problems from handwritten digit recognition and a large-scale human splice site data set from the domain of computational biology.

Keywords

Support Vector Machine Multiple Kernel Learning Handwritten Digit Hilbert Curve Candidate Motif 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  1. 1.Berlin Institute of TechnologyBerlinGermany
  2. 2.Department of Brain and Cognitive EngineeringKorea UniversitySeoulRepublic of Korea
  3. 3.Memorial Sloan-Kettering Cancer CenterNew YorkUSA
  4. 4.Humboldt University of BerlinBerlinGermany

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