Zusammenfassung
Im Rahmen eines vom BMFT geförderten Verbundprojektes „Wissensverarbeitung in neuronaler Architektur“ wird die Integration konnektionistischer Modelle mit symbolischer Wissensverarbeitung erforscht. Die anvisierten Bereiche für eine Integration sind: probabilistisches Schließen, die Einbeziehung. nichtsymbolischer Wissensquellen (Daten aus physikalischen Prozessen; Meßdaten), eine Uberführung gelernten Wissens in symbolische Form (maschinelles Lernen, Regelextraktion) und Information Retrieval.
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Literatur
Aertsen, A; Gerstein, G; Johannesma, P: From neuron to assembly: neuronal organization and stimulus representation. In: Palm, G; Aertsen, A (eds.): Brain Theory: pp. 7–24. Springer, Berlin 1986
Aertsen, A; Bonhoeffer, T; Krueger, J: Coherent activity in neuronal populations: analyis and interpretation. In: Caianiello, E.R. (ed.): Physics of cognitive processes. World Scientific Publishing, Singapore 1987
Anderberg, M.R: Cluster analysis for applications. Academic Press, New York 1973
Anderson, D.Z. (ed.): Neural information processing systems. Am. Inst. Phys., New York 1988
Antonisse, H.J., Keller, K.S.: Genetic Operators for High-Level Knowledge Representation, Proc 2nd Intern. Conf. on Genetic Algor., 1987, pp. 69–76
Baldi, P; Hornik, K: Neural networks and principal component analysis: learning from examples without local minima. Neural Networks 2: 53–58, 1989
Becks, K.H, Cremers, A.B., Hemker, A., Ultsch, A.: Parallel Process Interfaces to Knowledge Systems, Proc. ICNC, Düsseldorf 1990, pp 465–470.
Bentz, H J; Hagstroem, M; Palm, G: Information storage and effective data retrieval in sparse matrices. Neural Networks 2: 289–293, 1989
Biswas, G; Jain, A.K; Dubes, RC: Evaluation of projection algorithms. IEEE Trans. Pattern Anal. Machine Intell. PAMI-3: 701–708, 1981
Deichsel, G, Trampisch, H.J.: Clusteranalyse und Diskriminanzanalyse G.Fischer Verlag, Stuttgart, 1985
Eckmiller, R; v.d. Malsburg, C: Neural computers. Springer-Verlag, Berlin 1988
Erb, M; Palm, G.: Lernen und Informations- speicherung in neuronalen Netzen. In: Digitale Speicher, ITG-Fachbericht 102, ( W. Hilberg, ed.) VDE-Verlag GmbH, Berlin, Offenbach, 1988
Ernoult, C.: Performance of Backpropagation on a Parallel Transputer-Based Machine Proc. Neuro-Nimes 88,Nimes, France, pp.311–324
Forrest, S.: Implementing Semantic Network Structures Using the Classifier System Proc. Int. GA Conf, pp. 24–44, 1985
Fukunaga, K: Introduction to statistical pattern recognition. Academic Press, New York 1972
Genesereth, M, Nilsson N.: Logical Foundations of Artificial Intelligence, Morgan Kaufmann 1987
Gordon, A.D: Classification. Chapman & Hall Ltd., London 1981
Goser, K., U. Hilleringmann, Rückert, U., K. Schumacher: “VLSI Technologies for Artificial Neural Networks”, IEEE-Micro, Dec. 1989, pp. 28–44.
Jain, A.K; Dubes, R.C: Algorithms for clustering data. Prentice Hall, Englewood Cliffs, New Jersey, 1988
Kohonen, T: Self-organization and associative memory. Springer Series in Information Sciences 8, Heidelberg 1984
Krogh, A; Hertz, J.A: Hebbian learning of principal components. In: Eckmiller, R; Hartmann, G; Hauske, G (eds.): Parallel processing in neural systems and computers. Elsevier Science Publishers B.V., North-Holland 1990
Marks, K.M., Goser, K.F.: AI Concepts for VLSI Process Modelling and Monitoring Proc. Comp. Euro. 87, pp. 474–477, IEEE, 1987
Marks, K.M., Goser, K.F.: Analysis of VLSI Process Data Based on Self- organizing Feature Maps Proc. Neuro-Nimes 88,Nimes, France, pp. 337–348
McQueen, J: Some methods for classification and analysis of multivariate observations. Proc. 5th Berkeley Symp. Math. Stat. Prob. 281, 1967
Oja, E: A simplifies neuron model as a principal component analyser. J. Math. Biol. 15: 267–273, 1982
Oja, E: Neural netwoks: principal components, and subspaces. Int. J. Neural Systems 1: 61–68, 1989
Palm, G.: Neural Assemblies. An Alternative Approach to Artificial Intelligence. Springer-Verlag, Berlin, Heidelberg, New York, 1982
Palm, G.: Computing with Neural Networks. Science 235, 1227–1228, 1987
Palm, G.: Assoziatives Gedächtnis und Gehirntheorie. Spektrum der Wissenschaft, 54–64, Juni 1988
Palm, G.: Local Rules for Synaptic Modification in Neural Networks. In: Computational Neuroscience (E.L. Schwartz ed.). MIT-Press, Cambridge, London, 1990
Puppe, F.: Einführung in Expertensysteme, Springer Berlin 1988
Ramacher,U; Rückert, U. (eds.): “VLSI Design of Neural Networks”, Kluwer Academic, 1990.
Rückert, U., Ch. Kleerbaum, Goser, K.: “Digital VLSI Implementation of an Associative Memory based on Neural Networks”, Proceedings of the International Workshop on VLSI for Artificial Intelligence and Neural Networks, September 5–7, 1990, Oxford University.
Rückert, U.: “VLSI Implementation of an Associative Memory based on Distributed Storage of Information”, Proceedings of the ERAS IP Workshop on Neural Networks, Portugal 1990, Springer Verlag, Lecture Notes in Computer Science, 1990, pp. 267–276.
Sammon, J.W. jr.: A nonlinear mapping for data structure analysis. IEEE Trans. Computers C-18: 401–409, 1969
Siemon H.P., Ultsch A.: Kohonen Networks on Transputers: Implementation and Animation Accepted paper INNC 1990.
Sneath, P.H.A; Sokal, R.R: Numerical taxonomy. W.H. Freedman and Company Publishers, San Francisco 1973
Terekhina, A.Y: Methods of multidimensional data scaling and visualization (survey). Avtom. Telemekh. 7: 80–94, 1971
Ultsch, A.: Control for Knowledge based Information Retrieval, Verlag der Fachferine, Zürich 1987.
Ultsch, A.: Konnektionistische Modelle und ihre Integration mit wissensbasierten Systemen, Habilitationsschrift, Univ.Dortmund, 1991.
Ultsch, A. (Hrsg.): Kopplung deklarativer und konnektionistischer Wissensrepräsentation, Forschungsbericht Nr. 352, Institut für Informatik, Universität Dortmund, April 1990
Ultsch A., et al.: Optimizing Logical Proofs with Connectionist Networcs, Intl. Conf. Artificial Neural Networks, Helsinki, Juni 1991
Ultsch, A., Halmans, G., Mantyk, R.: CONCAT: A Connectionist Knowledge Ackquisition Tool, Proc. IEEE International Conference on System Sciences, January 9–11, Hawaii, 1991, pp 507–513.
Ultsch, A., Siemon, H.P.: Exploratory Data Analysis: Using Kohonen Networks on Transputers Forschungsbericht Nr. 329, Universität Dortmund 1989.
Ultsch, A., Siemon, H.P.: Kohonen’s Self Organizing Feature Maps for Exploratory Data Analysis, Proc. Intern. Neural Networks, Kluwer Academic Press, Paris, 1990, pp 305–308
Ultsch, A., Panda, PG.: Die Kopplung konnektionistischer Modelle mit wissensbasierten Systemen, Tagungsband Expertenystemtage Dortmund, Februar 1991.
Zahn, C.T: Graph-theoretical methods for detecting and describing Gestalt clusters. IEEE Trans. Computers C-20: 68–86, 1971
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Palm, G., Rückert, U., Ultsch, A. (1991). Wissensverarbeitung in neuronaler Architektur. In: Brauer, W., Hernández, D. (eds) Verteilte Künstliche Intelligenz und kooperatives Arbeiten. Informatik-Fachberichte, vol 291. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-76980-1_48
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