Prof.
Gerhard Ritter
University
of Florida, USA
Email: ritter@cise.ufl.edu
Qualifications
1971
Ph.D., University of Wisconsin-Madison, USA
Publications
(selected)
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G.X.
Ritter and G. Urcid, Perfect Recovery from Noisy Input Patterns with a
Dendritic Lattice Associative Memory, Proceedings of the
International Joint Conference on Neural Networks (IEEE/INNS), San Jose, CA,
2011, pp. 503-510.
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G.
X. Ritter and G. Urcid. “A Lattice Matrix Method for
Hyperspectral Image Unmixing,” Information Science, 181. Elsevier Science
Publishers B.V.: Amsterdam, 2011, pp. 1787-1803.
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G.
X. Ritter and G. Urcid. “Lattice Algebra Approach to Endmember Determination in
Hyperspectral Imagery.” Advances in Imaging and Electron Physics, Vol. 160. Ed.
P. Hawkes. Academic Press: Burlington, Massachusetts, 2010, pp. 113-169.
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G.
Urcid, J.C. Valdiviezo, G.X. Ritter, Lattice Associative Memories for
Segmenting Color Images in Different Color Spaces, IEEE Conference on Hybrid
Artificial Intelligence Systems (HAIS), San Sebastian, Spain, 2010, pp.
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G.X.
Ritter and G. Urcid, Learning in Lattice Neural Networks that Employ Dendritic
Computing, Computational Intelligence Based on Lattice Theory Vol. 67 of
Studies in Computational Intelligence. Eds. V.G. Kaburlasos and G.X. Ritter.
Springer Science+Business Media: Berlin Heidelberg, 2007, pp 25-44.
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G.
Urcid and G. X. Ritter. “Noise Masking for Pattern Recall Using a Single
Lattice Matrix Associative Memory.” Computational Intelligence Based on Lattice
Theory. Vol. 67 of Studies in Computational Intelligence. Edited by V.G.
Kaburlasos and G.X. Ritter. Springer Science+Business Media: Berlin Heidelberg,
2007, pp. 81-100.
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Computational
Intelligence Based on Lattice Theory Vol. 67 of Studies in Computational
Intelligence. Edited by V.G. Kaburlasos and G.X. Ritter. Springer
Science+Business Media: Berlin Heidelberg, 2007, pp. 45-58.
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G.X.
Ritter and L. Iancu. “A Lattice Algebra Approach to Neural Computation.” Handbook
of Computational Geometry for Pattern Recognition, Computer Vision,
Neurocomputing and Robotics. Springer Science+Business Media: Berlin
Heidelberg, 2005, pp. 97-129.
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G.X.
Ritter and L. Iancu, Lattice Algebra Approach to Neural Networks and Pattern Classification,”
Pattern Recognition and Image Analysis 14(2). Nauka/Interperiodica Publishing:
Moscow, 2004, pp. 191-198.
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G.X.
Ritter and L. Iancu. “Lattice Algebra Approach to Neural Networks and PPattern
Recognition, Image Analysis and Applications”. Vol. 3287 of Lecture Notes in
Computer Science. Springer Science+Business Media: Berlin Heidelberg, 2004, pp.
163-170.
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G.X.
Ritter and G. Urcid, Lattice Algebra Approach to Single-Neuron Computation,
IEEE Trans. Neural Networks, 14(2), 2003, pp.282-296.
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G.X.
Ritter, P. Gader, A.K. Hocaoglu, and L. Iancu. Automatic Acoustic Mine
Detection Using Morphological Perceptrons, Journal of the Acoustical Society of
America, 112(5), 2002.
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W.C.
Hu, K.H. Chang, G.X. Ritter, “Web Class: Web document classification using modified
decision trees,” in Proceedings 38th Annual ACM Southeast Conference, Clemson,
SC., April 2000, pp. 262-263.
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J.N.
Wilson, G.X. Ritter, E.J. Riedy. An Image Algebra Based SIMD Image Processing
Environment, in VISUAL COMMUNICATIONS and IMAGE PROCESSING PROCESSING, Chapter
17, Marcel Dekker Inc., New York, 1999.
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W.C.
Hu, G.X. Ritter, and M.S. Schmalz, “Approximating the Longest Approximate
Common Subsequence Problem,” in Proceedings of the 36th Annual Southeast ACM
Conference, Marietta, GA., April, 1998, pp. 166-172.
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G.X.
Ritter and J.N. Wilson. Handbook of Cpmputer Vision Algorithmd in Image
Algebra, CRC Press, Boca Raton, FL., 1996.
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H.
Shi, G.X. Ritter, and J.N. Wilson. “Parallel Image Processing with Image
Algebra on SIMD Mesh-connected Computers.” Advances in Imaging and Electron
Physics, Vol. 90. Edited by P. Hawkes. Academic Press: New York, New York,
1995.