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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.
From Bernhard Schölkopf 1
1Max Planck Institute for Intelligent Systems, Tübingen, Germany
(Received 18.03.2011; accepted 04.05.2011)
Appeared in International Journal of Materials Research 2011/07, Page 809-814
DOI: 10.3139/146.110530
Direct link: http://www.ijmr.de/MK110530
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References
1 G.W. Leibniz: Discours de métaphysique. (cited after Chaitin, 2010), 1686.
2 H.B. Barlow, in: M. Gazzaniga (Ed.), The neuron doctrine in perception. The Cognitive Neurosciences, MIT Press, Cambridge, MA, 1995.
3 H. Helmholtz: Handbuch der physiologischen Optik, Leopold Voss, Leipzig, 1867.
4 S. Sonnenburg, G. Rätsch, C. Schäfer, B. Schölkopf: Journal of Machine Learning Research 7 (2006) 1531 –1565.
5 B. Schölkopf, A.J. Smola: Learning with Kernels, MIT Press, Cambridge, MA, 2002.
6 M. Hutter: Universal artificial intelligence, Springer, Berlin, 2005.
7 J. Watkins: Karl Raimund Popper 1902–1994. Proceedings of the British Academy 96 (1997) 645–684.
8 V.N. Vapnik: Statistical Learning Theory, Wiley, New York, 1998.
9 C. Walder, B. Schölkopf, O. Chapelle: Comput. Graphics Forum 25 (2006) 635–644. (Eurographics).
10 B. Schölkopf, F. Steinke, V. Blanz, in: L. De Raedt, S. Wrobel Eds., Object correspondence as a machine learning problem. Proceedings of the 22ndInternational Conference on Machine Learning, pages 777 –784, New York, 2005. ACM Press.
11 M. Hofmann, F. Steinke, V. Scheel, G. Charpiat, J. Farquhar, P. Ascho, M. Brady, B. Schölkopf, B.J. Pichler: J. Nucl. Med. 49 (2008) 1875–1883.
12 J. Kober, K. Mülling, O. Krömer, C.H. Lampert, B. Schölkopf, J. Peters: Movement templates for learning of hitting and batting, in: Proceedings of the 2010 IEEE International Conference on Robotics and Automation (ICRA), pages 853–858, Piscataway, NJ, U. S. A., 2010. IEEE.
13 P. Daniušis, D. Janzing, J. Mooij, J. Zscheischler, B. Steudel, K. Zhang, B. Schölkopf: Inferring deterministic causal relations, in: 26thConference on Uncertainty in Artificial Intelligence, Corvallis, OR, U. S. A., 07 2010. AUAI Press.
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