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Towards Integrative Machine Learning and Knowledge Extraction pp 1–12Cite as

Towards Integrative Machine Learning and Knowledge Extraction

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Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 10344))

Abstract

This Volume is a result of workshop 15w2181 “Advances in interactive knowledge discovery and data mining in complex and big data sets” at the Banff International Research Station for Mathematical Innovation and Discovery. The workshop was dedicated to bring together experts with diverse backgrounds but with one common goal: to understand intelligence for the successful design, development and evaluation of algorithms that can learn from data, extract knowledge from experience, and to improve their learning behaviour over time – similarly as we humans do. Knowledge discovery, data mining, machine learning, artificial intelligence are more or less synonymously used with no strict definitions or boundaries. “Integrative” means to support not only the machine learning & knowledge extraction pipeline, ranging from dealing with data in arbitrarily high-dimensional spaces to the visualization of results into a lower dimension accessible to a human; it is taking into account seemingly disparate fields which can be very fruitful when brought together - for solving problems in complex application domains (e.g. health informatics). Here we want to emphasize that the most important findings in machine learning will be those we do not know yet. In this paper we provide: (1) a short motivation for the integrative approach; (2) brief summaries of the presentations given in Banff; and (3) some personally flavoured, subjective future research outlooks, e.g. in the combination of geometrical approaches with machine learning.

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Acknowledgements

We are grateful to all participants of the Banff BIRS workshop 15w2181, specifically to our colleagues from the international HCI-KDD expert network and generally to all colleagues who constantly support our group in fostering the idea of an integrated machine learning approach and in bringing together diverse areas in an cross-disciplinary manner to stimulate fresh ideas and to encourage multi-disciplinary problem solving. The past has shown that many new discoveries are made in overlapping areas of seemingly disjunct fields and the interesting and most important discoveries are those which we have not yet found.

Author information

Authors and Affiliations

  1. Holzinger Group, HCI-KDD, Institute for Medical Informatics/Statistics, Medical University Graz, Graz, Austria

    Andreas Holzinger

  2. Centre for Machine Learning, University of Alberta, Edmonton, Canada

    Randy Goebel

  3. Cogent Computing Applied Research Centre, Coventry University, Coventry, UK

    Vasile Palade

  4. Vision Mathematics Group, Department of Mathematics, University of Bologna, Bologna, Italy

    Massimo Ferri

Authors
  1. Andreas Holzinger
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  2. Randy Goebel
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  3. Vasile Palade
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  4. Massimo Ferri
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Corresponding author

Correspondence to Andreas Holzinger .

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Editors and Affiliations

  1. Medical University Graz, Graz, Austria

    Andreas Holzinger

  2. University of Alberta, Edmonton, Alberta, Canada

    Randy Goebel

  3. Bologna University, Bologna, Italy

    Massimo Ferri

  4. Coventry University, Coventry, United Kingdom

    Vasile Palade

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Holzinger, A., Goebel, R., Palade, V., Ferri, M. (2017). Towards Integrative Machine Learning and Knowledge Extraction. In: Holzinger, A., Goebel, R., Ferri, M., Palade, V. (eds) Towards Integrative Machine Learning and Knowledge Extraction. Lecture Notes in Computer Science(), vol 10344. Springer, Cham. https://doi.org/10.1007/978-3-319-69775-8_1

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  • DOI: https://doi.org/10.1007/978-3-319-69775-8_1

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