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  Empirical Inference

Schölkopf, B. (2011). Empirical Inference. International Journal of Materials Research, 2011(7), 809-814. doi:10.3139/146.110530.

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Schölkopf, B1, Author              
Affiliations:
1Dept. Empirical Inference, Max Planck Institute for Intelligent Systems, Max Planck Society, ou_1497647              

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 Abstract: Empirical Inference is the process of drawing conclusions from observational data. For instance, the data can be measurements from an experiment, which are used by a researcher to infer a scientific law. Another kind of empirical inference is performed by living beings, continuously recording data from their environment and carrying out appropriate actions. Do these problems have anything in common, and are there underlying principles governing the extraction of regularities from data? What characterizes hard inference problems, and how can we solve them? Such questions are studied by a community of scientists from various fields, engaged in machine learning research. This short paper, which is based on the author’s lecture to the scientific council of the Max Planck Society in February 2010, will attempt to describe some of the main ideas and problems of machine learning. It will provide illustrative examples of real world machine learning applications, including the use of machine learning towards the design of intelligent systems.

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 Dates: 2011-07
 Publication Status: Published in print
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 Rev. Type: -
 Identifiers: DOI: 10.3139/146.110530
BibTex Citekey: Scholkopf2011
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Title: International Journal of Materials Research
  Abbreviation : Int. J. Mat. Res.
Source Genre: Journal
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Publ. Info: München, Germany : Hanser
Pages: - Volume / Issue: 2011 (7) Sequence Number: - Start / End Page: 809 - 814 Identifier: ISSN: 1862-5282
CoNE: https://pure.mpg.de/cone/journals/resource/954925453910