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Jstacs: a java framework for statistical analysis and classification of biological sequences

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Ivo GrosseAuthors Info & Claims

The Journal of Machine Learning Research, Volume 13, Issue 1

Pages 1967 - 1971

Published: 01 June 2012 Publication History

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Abstract

Jstacs is an object-oriented Java library for analysing and classifying sequence data, which emerged from the need for a standardized implementation of statistical models, learning principles, classifiers, and performance measures. In Jstacs, these components can be used, combined, and extended easily, which allows for a direct comparison of different approaches and fosters the development of new components. Jstacs is especially tailored to biological sequence data, but is also applicable to general discrete and continuous data. Jstacs is freely available at http://www.jstacs.de under the GNU GPL license including an API documentation, a cookbook, and code examples.

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Cited By

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Eggeling RGrosse IKoivisto M(2019)Algorithms for learning parsimonious context treesMachine Language10.1007/s10994-018-5770-9108:6(879-911)Online publication date: 17-Jul-2019
https://dl.acm.org/doi/10.1007/s10994-018-5770-9
Eggeling RKoivisto M(2016)Pruning rules for learning parsimonious context treesProceedings of the Thirty-Second Conference on Uncertainty in Artificial Intelligence10.5555/3020948.3020965(152-161)Online publication date: 25-Jun-2016
https://dl.acm.org/doi/10.5555/3020948.3020965
Kamath ULin JDe Jong KArnold D(2014)SAX-EFGProceedings of the 2014 Annual Conference on Genetic and Evolutionary Computation10.1145/2576768.2598321(533-540)Online publication date: 12-Jul-2014
https://dl.acm.org/doi/10.1145/2576768.2598321

Index Terms

Jstacs: a java framework for statistical analysis and classification of biological sequences
1. Computing methodologies
  1. Machine learning
    1. Learning paradigms
      1. Supervised learning
        Supervised learning by classification
    2. Machine learning approaches
      1. Classification and regression trees
2. Software and its engineering
  1. Software notations and tools
    1. General programming languages
      1. Language features
        Frameworks
      2. Language types

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cover image The Journal of Machine Learning Research

The Journal of Machine Learning Research Volume 13, Issue 1

January 2012

3712 pages

ISSN:1532-4435

EISSN:1533-7928

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JMLR.org

Publication History

Published: 01 June 2012

Published in JMLR Volume 13, Issue 1

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Cited By

View all

Eggeling RGrosse IKoivisto M(2019)Algorithms for learning parsimonious context treesMachine Language10.1007/s10994-018-5770-9108:6(879-911)Online publication date: 17-Jul-2019
https://dl.acm.org/doi/10.1007/s10994-018-5770-9
Eggeling RKoivisto M(2016)Pruning rules for learning parsimonious context treesProceedings of the Thirty-Second Conference on Uncertainty in Artificial Intelligence10.5555/3020948.3020965(152-161)Online publication date: 25-Jun-2016
https://dl.acm.org/doi/10.5555/3020948.3020965
Kamath ULin JDe Jong KArnold D(2014)SAX-EFGProceedings of the 2014 Annual Conference on Genetic and Evolutionary Computation10.1145/2576768.2598321(533-540)Online publication date: 12-Jul-2014
https://dl.acm.org/doi/10.1145/2576768.2598321

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