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Wissenserwerb

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Künstliche Intelligenz in Bild- und Sprachanalyse

Part of the book series: Leitfäden und Monographien der Informatik ((LMI))

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Zusammenfassung

In diesem Kapitel werden Verfahren vorgestellt, die es erlauben, Wissen aus einem konkreten Problemkreis in einen der oben erörterten Repräsentationsformalismen einzubringen. Man bezeichnet dieses als Wissensakquisition, Wissenserwerb oder Lernen, wobei letzterer Begriff in der Regel nur verwendet wird, wenn der Wissenserwerb automatisch durch das Analysesystem erfolgt, wie in Bild 1.3 angedeutet. Zunächst werden einige Formen des Wissenserwerbs abgegrenzt und die hier verwendeten Begriffe definiert. Es wird betont, daß dieses Kapitel nicht Lernen an sich, sondern im Kontext der Bild- und Sprachanalyse behandelt.

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Bibliografischer Rückblick

  1. Berwick, R.C.: Locality Principle and the Acquisition of Syntactic Knowledge. MIT Press Cambridge 1982

    Google Scholar 

  2. Bunke, H., Allermann, G.: Inexact Graph Matching for Structural Pattern Recognition. Pattern Recognition Letters 1 (1983) 245–253

    Article  MATH  Google Scholar 

  3. Crowley, J.L.: Navigation for an Intelligent Mobile Robot. IEEE Trans. RA -1 (1985) 31–41

    Google Scholar 

  4. Davis, R.: Interactive Transfer of Expertise: Acquisition of New Inference Rules. AI 12 (1979) 121–157

    Google Scholar 

  5. Duda, R.O., Hart, P.E.: Pattern Classification and Scene Analysis. J. Wiley New York 1972

    Google Scholar 

  6. Fu, K.S., Booth, T.L.: Grammatical Inference: Introduction and Survey. IEEE Trans. SMC 5, Part I 95–111, Part II 409–423 (1975)

    Google Scholar 

  7. Hermann, M., Kanade, T., Kuroe, S.: Incremental Acquisition of a Three–Dimensional Scene Model from Images. IEEE Trans. PAMI 6 (1984) 331–340

    Article  Google Scholar 

  8. Holland, J.H.: Adaptation in Natural and Artificial Systems. Univ. of Michigan Press, Ann Arbor 1975

    Google Scholar 

  9. Michalski, R.S.: Pattern Recognition as Rule-Guided Inference. IEEE Trans. PAMI 2 (1980) 349–361

    Article  MATH  Google Scholar 

  10. Michalsky, R.S.; Carbonell, J.G.; Mitchell, T.M.: Machine Learning, An Artificial Intelligence Approach. Tioga, Palo Alto, California 1983

    Google Scholar 

  11. Niemann, H.: Klassifikation von Mustern. Springer, Berlin Heidelberg New York Tokyo 1983

    Book  MATH  Google Scholar 

  12. Perkins, W.A.: INSPECTOR: A Computer Vision System that Learns to Inspect Parts. IEEE Trans. PAMI 5 (1983) 584–592

    Article  Google Scholar 

  13. Politakis, P.; Weiss, S.M.: Using Empirical Analysis to Refine Expert Systems Knowledge Data Bases. AI 22 (1984) 23–48

    Google Scholar 

  14. Shapiro, L.G.: Haralick, R.M.: A Metric for Comparing Relational Descriptions. IEEE Trans. PAMI 7 (1985) 90–94

    Article  Google Scholar 

  15. Smith, S.F.: Flexible Learning of Problem Solving Heuristics Through Adaptive Search. Proc. 8. IJCAI, Karlsruhe 1983

    Google Scholar 

  16. Vere, S.A.: Induction of Concepts in the Predicate Calculus. Advance Papers of 4. IJCAI, Tbilisi, Georgia, USSR 1975, 281–287

    Google Scholar 

  17. Vere, S.A.: Multilevel Counterfactuals for Generalization of Relational Concepts and Productions. AI 14 (1980) 139–164

    MATH  Google Scholar 

  18. Winston, P.H.: Learning by Creatifying Transfer Frames. AI 10 (1978) 147–172

    Google Scholar 

  19. Winston, P.H.: Learning Structural Descriptions from Examples. In P.H. Winston (ed.): The Psychology of Computer Vision. Mc Graw Hill, New York 1975, 157–209

    Google Scholar 

  20. Yachida, M.; Tsuji, S.: A Versatile Machine Vision System for Complex Industrial Parts. IEEE Trans. C 26 (1977) 882–894

    Article  Google Scholar 

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Author information

Authors and Affiliations

  1. Universität Erlangen-Nürnberg, Erlangen, Deutschland

    Prof. Dr.-Ing. Heinrich Niemann (Professor)

  2. Universität Bern, Bern, Schweiz

    Prof. Dr.-Ing. Horst Bunke (Professor)

Authors
  1. Prof. Dr.-Ing. Heinrich Niemann
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  2. Prof. Dr.-Ing. Horst Bunke
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© 1987 B. G. Teubner Stuttgart

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Niemann, H., Bunke, H. (1987). Wissenserwerb. In: Künstliche Intelligenz in Bild- und Sprachanalyse. Leitfäden und Monographien der Informatik. Vieweg+Teubner Verlag, Wiesbaden. https://doi.org/10.1007/978-3-322-96664-3_7

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  • DOI: https://doi.org/10.1007/978-3-322-96664-3_7

  • Publisher Name: Vieweg+Teubner Verlag, Wiesbaden

  • Print ISBN: 978-3-519-02261-9

  • Online ISBN: 978-3-322-96664-3

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