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Machine Learning Background

In: Representation Learning

Author

Listed:
  • Nada Lavrač

    (Jožef Stefan Institute, Department of Knowledge Technologies
    University of Nova Gorica, School of Engineering and Management)

  • Vid Podpečan

    (Jožef Stefan Institute, Department of Knowledge Technologies)

  • Marko Robnik-Šikonja

    (University of Ljubljana, Faculty of Computer and Information Science)

Abstract

This chapter provides an introduction to standard machine learning approaches that learn from tabular data representations, followed by an outline of approaches using various other data types addressed in this monograph: texts, relational databases, and networks (graphs, knowledge graphs, and ontologies). We first briefly sketch the historical outline of the research area, establish the basic terminology, and categorize learning tasks in Sect. 2.1. Section 2.2 provides a short introduction to text mining. Section 2.3 introduces relational learning techniques, followed by a brief introduction to network analysis, including semantic data mining, in Sect. 2.4. The means for evaluating the performance of machine learning algorithms, when used for prediction and rule quality estimation, are outlined in Sect. 2.5. We outline selected data mining techniques and platforms in Sect. 2.6. Finally, Sect. 2.7 presents the implemented software that allows for running selected methods on illustrative examples.

Suggested Citation

  • Nada Lavrač & Vid Podpečan & Marko Robnik-Šikonja, 2021. "Machine Learning Background," Springer Books, in: Representation Learning, chapter 0, pages 17-53, Springer.
    • Handle: RePEc:spr:sprchp:978-3-030-68817-2_2
    DOI: 10.1007/978-3-030-68817-2_2
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