Referenzen

Bücher

  1. M. J. Zaki and W. Meira, Data Mining and Machine Learning - Fundamental Concepts and Algorithms. Cambridge University Press, 2020.
  2. C. R. Farrar and K. Worden, Structural Health Monitoring: A Machine Learning Perspective. Wiley-Interscience, 2013.
  3. X.-S. Yang, Introduction to Algorithms for Data Mining and Machine Learning. Elsevier, 2019.
  4. M. Sugiyama, Introduction to Statistical Machine Learning. 2016.
  5. J. Herrmann, Maschinelles Lernen und Wissensbasierte Systeme. Springer, 1997.
  6. R. Zafarani, M. A. Abbasi, and H. Liu, Social Data Mining. 2014.
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  1. I. H. Witten, E. Frank, M. A. Hall, and C. J. Pal, Data Mining Practical Machine Learning Tools and Techniques. Morgan Kaufmann.
  2. C. Borcea, M. Talasila, and R. Curtmola, Mobile Crowdsensing. CRC Press, 2017.
  3. J. G. Webster, Measurement, Instrumentation and Sensors Handbook, no. 0. 1999.
  4. L. Rokach and O. Maimon, Data Mining with Decision Trees - Theory and Applications. World Scientific Publishing, 2015.
  5. N. J. Nilsson, Introduction To Machine Learning. 1996.
  6. T. M. Mitchel, Machine Learning. McGraw Hill, 1997.
  7. P. Attewell and D. B. Monaghan, Data mining for the social sciences : an introduction. University of California Press, 2015.
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  1. J. R. Quinlan, “Induction of Decision Trees,” in Machine Learning, Kluwer Academic Publishers, Boston, 1986
  2. T. Mueller, A. G. Kusne, and R. Ramprasad, “Machine learning in materials science: Recent progress and emerging applications,” Reviews in Computational Chemistry, Volume 29, First Edition., 2016.
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Artikel

  1. T. Mueller, A. G. Kusne, and R. Ramprasad, “Machine learning in materials science: Recent progress and emerging applications,” Reviews in Computational Chemistry, Volume 29, First Edition., 2016.

  2. N.-C. Chen et al., “Challenges of Applying Machine Learning to Qualitative Coding.”

  3. J. Radford, K. Joseph, "Theory In, Theory Out: The Uses of Social Theory in Machine Learning for Social Science", Front. Big Data

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