Barbara Hammer

	Institute of Computer Science - Computational Intelligence Group
	Prof. Dr. Barbara Hammer

Publications
books - journals - conferences - reports - varia

(Please note: some of the articles are subject to copyright restrictions in which case a preliminary version is available.)

Books and book chapters:

B.Hammer, P.Hitzler (eds.), Perspectives of Neural-Symbolic Integration, Springer, 2007.
B.Hammer, A.Micheli, A.Sperduti, Adaptive Contextual Processing of Structured Data by Recursive Neural Networks: A Survey of Computational Properties, in: B.Hammer, P. Hitzler (eds.), Perspectives of Neural-Symbolic Integration, Springer, 2007.
P.Tino, B.Hammer, M.Boden, Markovian Bias of Neural-based Architectures, in: B.Hammer, P. Hitzler (eds.), Perspectives of Neural-Symbolic Integration, Springer, 2007.
B.Hammer, M.Strickert, T.Villmann, Prototype based recognition of splice sites, in U.Seiffert, L.C.Jain, P.Schweitzer, Bioinformatics using computational intelligence, 25-55, Springer, 2005
B. Hammer, Perspectives on Learning Symbolic Data with Connectionistic Systems, R.Kühn, R.Menzel, W.Menzel, U.Ratsch, M.M.Richter, I.-O.Stamatescu, Adaptivity and Learning, 141-160, Springer, 2003.
B. Hammer, Compositionality in Neural Systems, M. Arbib, Handbook of Brain Theory and Neural Networks, 2nd edition, 244-248, 2002.
B. Hammer, Learning with Recurrent Neural Networks, Springer Lecture Notes in Control and Information Sciences 254, Springer-Verlag, 2000
(This constitutes a considerably extended version of my PhD thesis)
V. Sperschneider, B. Hammer, Theoretische Informatik - Eine problemorientierte Einführung, Springer-Verlag, 1996
pdf file, Errata

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Journal articles:

F.-M. Schleif, Th. Villmann, B. Hammer, Prototype based fuzzy classification in clinical proteomics, International Journal of Approximate Reasoning 47(1): 4-16, 2008.
M.Biehl, A.Gosh, B.Hammer, Dynamics and generalization ability of LVQ algorithms, Journal of Machine Learning Research 8:323-360, 2007.
B.Hammer, A.Hasenfuss, T.Villmann, Magnification control for batch neural gas, Neurocomputing 70(7-9):1225-1234, 2007.
F.-M. Schleif, B. Hammer, Th. Villmann, Margin based Active Learning for LVQ Networks, Neurocomputing 70(7-9):1215-1224, 2007.
M.Biehl, A.Gosh, B.Hammer, Learning Vector Quantization: the dynamics of Winner-Takes-All algorithms, Neurocomputing 69(7-9):660-670, 2006.
A. Ghosh, M. Biehl, B. Hammer, Performance analysis of LVQ algorithms: a statistical physics approach, Neural Networks 19:817-829, 2006.
T.Villmann, F.-M.Schleif, B.Hammer, Prototype-based fuzzy classification with local relevance for proteomics, Neurocomputing 69(16-18):2425-2428, 2006.
T.Villmann, F.-M.Schleif, B.Hammer, Comparison of Relevance Learning Vector Quantization with other Metric Adaptive Classification Methods, Neural Networks 19:610-622, 2006.
M. Cottrell, B. Hammer, A. Hasenfuss, T. Villmann, Batch and Median Neural Gas, Neural Networks 19:762-771, 2006.
T. Villmann, B. Hammer, F.-M. Schleif, T. Geweniger, W. Herrmann, Fuzzy Classification by Fuzzy Labeled Neural Gas, Neural Networks 19:772-779, 2006.
M.Strickert, U.Seiffert, N.Sreenivasulu, W.Weschke, T.Villmann, B.Hammer, Generalized Relevance LVQ (GRLVQ) with Correlation Measures for Gene Expression Analysis, Neurocomputing 69, 651-659, 2006.
B. Hammer, Th. Villmann, Effizient Klassifizieren und Clustern: Lernparadigmen von Vektorquantisierern, Küunstliche Intelligenz 6(3):5-11, 2006.
B.Hammer, A.Micheli, A.Sperduti, Universal approximation capability of cascade correlation for structures, Neural Computation 17:1109-1159, 2005.
M.Strickert, B.Hammer, Merge SOM for temporal data, Neurocomputing 64:39-72, 2005.
B.Hammer, A.Micheli, A.Sperduti, M.Strickert, Recursive self-organizing network models, Neural Networks 17(8-9), 1061-1086, 2004.
M.Strickert, B.Hammer, S.Blohm, Unsupervised recursive sequences processing, Neurocomputing 63, 69-98, 2005.
K.Gersmann, B.Hammer, Improving iterative repair strategies for scheduling with the SVM, Neurocomputing 63, 271-292, 2005.
B.Hammer, M.Strickert, T.Villmann, Supervised neural gas with general similarity measure, Neural Processing Letters 21(1), 21-44, 2005.
B.Hammer, M.Strickert, T.Villmann, On the generalization ability of GRLVQ networks, Neural Processing Letters 21(2):109-120, 2005.
B. DasGupta, B. Hammer, On approximate learning by multi-layered feedforward circuits, Theoretical Computer Science 348 (1):95-127, 2005.
B.Hammer, A.Micheli, M.Strickert, A.Sperduti, A general framework for unsupervised processing of structured data, Neurocomputing 57, 3-35, 2004.
P. Tino, B. Hammer, Architectural Bias in Recurrent Neural Networks - Fractal Analysis, Neural Computation 15(8), 1931-1957, 2003.
B. Hammer, P. Tino, Recurrent neural networks with small weights implement definite memory machines, Neural Computation 15(8), 1897-1929, 2003.
B. Hammer, K. Gersmann, A note on the universal approximation capability of support vector machines, Neural Processing Letters 17, 43-53, 2003.
T. Villmann, E. Merenyi, B. Hammer, Neural maps in remote sensing image analysis, Neural Networks 16(3-4),389-403, 2003.
B. Hammer, T. Villmann, Generalized relevance learning vector quantization, Neural Networks 15, 1059-1068, 2002.
B. Hammer, Recurrent networks for structured data - a unifying approach and its properties, Cognitive Systems Research 3(2), 145-165, 2002
B. Hammer, Generalization ability of folding networks, IEEE Transactions on Knowledge and Data Engineering, 13(2): 196-206, 2001
M. Vidyasagar, S. Balaji, B. Hammer, Closure properties of uniform convergence of empirical means and PAC learnability under a family of probability measures, System and Control Letters 42, 151-157, 2001
B. Hammer, On the approximation capability of recurrent neural networks, Neurocomputing, 31(1-4): 107-124, 2000
B. Hammer, On the learnability of recursive data, Mathematics of Control Signals and Systems, 12: 62-79, 1999

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Contributions to international conferences:

F.-M.Schleif, T.Villmann, B.Hammer, Local Metric Adaptation for Soft Nearest Prototype Classification to Classify Proteomic Data, Fuzzy Logic and Applications, Lecture Notes in Computer Science 3849, Isabelle Bloch, Alfredo Petrosino, Andrea G.B. Tettamanzi, Springer, 290-296, 2006
T.Villmann, F.-M.Schleif, B.Hammer, Fuzzy Labeled Soft Nearest Neighbor Classification with Relevance Learning, to appear at ICMLA 2005
M.Cottrell, B.Hammer, A.Hasenfuß, T.Villmann, Batch NG, in: Proceedings of WSOM 2005, Paris, France, 275-282, September 05-08, 2005
B.Hammer, A.Micheli, N.Neubauer, A.Sperduti, M.Strickert, Self Organizing Maps for Time Series, in: Proceedings of WSOM 2005, Paris, France, 115-122, September 05-08, 2005 (invited plenary)
T.Villmann, B.Hammer, F.-M.Schleif, T.Geweniger, T. Fuzzy Labeled Neural Gas for Fuzzy Classification, in: Proceedings of WSOM 2005, Paris, France, 283-290, September 05-08, 2005
A. Ghosh, M. Biehl, B. Hammer, Dynamical Analysis of LVQ type learning rules, in: Proc. of the 5th Workshop on Self-Organizing Maps WSOM'05 (Paris, 2005), 578-594.
B.Hammer, T.Villmann, Classification using non standard metrics, ESANN'05, M. Verleysen (ed.), d-side publishing (2005), 303-316.
M.Biehl, A.Gosh, B.Hammer, The dynamics of Learning Vector Quantization, ESANN'05, M. Verleysen (ed.), d-side publishing (2005), 13-18.
K.Tluk von Toschanowitz, B.Hammer, H.Ritter, Relevance determination in reinforcement learning, ESANN'05, M. Verleysen (ed.), d-side publishing (2005), 369-374.
B.Hammer, A.Rechtien, M.Strickert, T.Villmann, Relevance learning for mental disease classification, ESANN'05, M. Verleysen (ed.), d-side publishing (2005), 139-144.
F.-M.Schleif, U.Clauss, T.Villmann, B.Hammer, Supervised Relevance Neural Gas and Unified Maximum Separability Analysis for Classification of Mass Spectrometric Data, in: Proceedings of ICMLA 2004, Louisville, KY, USA, Publisher: IEEE Press, ISBN 0-7803-9923-2, pages 374-379, December 16-18, 2004.
T.Villmann, F.Schleif, B.Hammer, Metric adaptation for optimal feature classification in learning vector quantization applied to environment detection, in: H.-M. Groß, K. Debes, H.-J. Böhme (Eds.), SOAVE 2004, 3rd Workshop on SelfOrganization of AdaptiVE Behavior, Fortschritts-Berichte VDI Reihe 10, Nr. 742, VDI Verlag, Düsseldorf, 2004.
K.Tluk von Toschanowitz, B.Hammer, H.Ritter, , Mapping the Design Space of Reinforcement Learning Problems - a Case Study, in: H.-M. Groß, K. Debes, H.-J. Böhme (Eds.), SOAVE 2004, 3rd Workshop on SelfOrganization of AdaptiVE Behavior, Fortschritts-Berichte VDI Reihe 10, Nr. 742, VDI Verlag, Düsseldorf, 251-261, 2004.
K. Gersmann, B. Hammer, A reinforcement learning algorithm to improve scheduling search heuristics with the SVM, IJCNN'2004
K. Gersmann, B. Hammer, Improving iterative repair strategies for scheduling with the SVM, in: M.Verleysen (ed.), European Symposium on Artificial Neural Networks'2003, D-side publications, 235-240, 2003.
B.Hammer, M.Strickert, T.Villmann, Relevance LVQ versus SVM, in: L.Rutkowski, J.Siekmann, R.Tadeusiewicz, L.A.Zadeh, Artificial Intelligence and Softcomputing, Springer Lecture Notes in Artificial Intelligence 3070, 592-597, 2004.
M.Strickert, B.Hammer, Self-organizing context learning, in: M.Verleysen (ed.), European Symposium at Artificial Neural Networks'2004, D-side publications, 39-44, 2004.
B.Hammer, B.J.Jain, Neural methods for non-standard data, in: M.Verleysen (ed.), European Symposium at Artificial Neural Networks'2004, D-side publications, 281-292, 2004.
T.Villmann, F.M.Schleif, B.Hammer, Supervised Neural Gas and Relevance Learning in Learning Vector Quantization, WSOM'03, 47-52, 2003.
M. Strickert, B. Hammer, Neural Gas for Sequences, WSOM'03, 53-57, 2003.
T. Bojer, B. Hammer, M. Strickert, and T. Villmann, Determining relevant input dimensions for the self-organizing map, in: L. Rutkowski and J. Kacprzyk (eds.), Neural Networks and Soft Computing (Proc. ICNNSC 2002), Physica-Verlag, 388-393, 2003.
M.Strickert, B.Hammer, Unsupervised recursive sequence processing, in: M.Verleysen (ed.), European Symposium on Artificial Neural Networks'2003, D-side publications, 27-32, 2003.
T. Bojer, B. Hammer, C. Koers, Monitoring technical systems with prototype based clustering, in: M.Verleysen (ed.), European Symposium on Artificial Neural Networks'2003, D-side publications, 433-439, 2003.
B. Hammer, T. Villmann, Mathematical aspects of neural networks, in: M.Verleysen (ed.), European Symposium on Artificial Neural Networks'2003, D-side publications, 59-72, 2003.
P. Tino, B. Hammer, Architectural bias in recurrent neural networks - fractal analysis, in: J.R.Dorronsoro (Ed.), Artificial Neural Networks -- ICANN 2002, Springer, 1359-1364, 2002. (Best conference paper award)
B. Hammer, A. Rechtien, M. Strickert, T. Villmann, Rule extraction from self-organizing maps, in: J.R.Dorronsoro (Ed.), Artificial Neural Networks -- ICANN 2002, Springer, 877-882, 2002.
B. Hammer, M. Strickert, T. Villmann, Learning vector quantization for multimodal data, in: J.R.Dorronsoro (Ed.), Artificial Neural Networks -- ICANN 2002, Springer, 370-375, 2002.
B. Hammer, J.J. Steil, Perspectives on learning with recurrent networks, in: M.Verleysen (ed.), European Symposium on Artificial Neural Networks'2002, D-side publications, 357-368, 2002
B. Hammer, T. Villmann, Batch-RLVQ, in: M.Verleysen (ed.), European Symposium on Artificial Neural Networks'2002, D-side publications, 295-300, 2002
B. Hammer, A. Micheli, A. Sperduti, A general framework for unsupervised processing of structured data, in: M.Verleysen (ed.), European Symposium on Artificial Neural Networks'2002, D-side publications, 389-394, 2002
T. Villmann, B. Hammer, M. Strickert, Supervised neural gas for learning vector quantization, in: D.Polani, J.Kim, T.Martinetz (eds.), Fifth German Workshop on Artificial Life, IOS Press, 9-18, 2002
M. Strickert, T. Bojer, B. Hammer, Generalized relevance LVQ for time series, in: G.Dorffner, H.Bischof, K.Hornik (eds.), Artificial Neural Networks - ICANN'2001, Springer, 677-683, 2001
B. Hammer, On the generalization ability of recurrent networks, in: G.Dorffner, H.Bischof, K.Hornik (eds.), Artificial Neural Networks - ICANN'2001, Springer, 731-736, 2001
B. Hammer, T. Villmann, Estimating relevant input dimensions for self-organizing algorithms, in: N.Allison, H.Yin, L.Allinson, J.Slack (eds.), Advances in Self-Organizing Maps, Springer, 173-180, 2001
B. Hammer, T. Villmann, Input pruning for neural gas architectures, in: M.Verleysen (ed.), European Symposium on Artificial Neural Networks'2001, D-facto publications, 283-288, 2001
T. Bojer, B. Hammer, D. Schunk, K. Tluk von Toschanowitz, Relevance determination in learning vector quantization, in: M.Verleysen (ed.), European Symposium on Artificial Neural Networks'2001, D-facto publications, 271-276, 2001
B. DasGupta, B. Hammer, On approximate learning by multi-layered feedforward circuits, in: H. Arimura, S. Jain, A. Sharma (eds.), Algorithmic Learning Theory'2000, Springer, 264-278, 2000
B. Hammer, Limitations of hybrid systems, in: M.Verleysen (ed.), European Symposium on Artificial Neural Networks'2000, D-facto publications, 213-218, 2000
B. Hammer, Approximation capabilities of folding networks, in: M. Verleysen (ed.), European Symposium on Artificial Neural Networks'99, D-facto publications, 33-38, 1999
B. Hammer, On the Approximation Capability of Recurrent Neural Networks (Best session presentation award), in: M. Heiss (ed.), International Symposium on Neural Computation'98, ICSC Academic Press, 512-518, 1998
B. Hammer, Some complexity results for perceptron networks, in: L. Niklasson, M. Boden, T. Ziemke (eds.), International Conference on Artificial Neural Networks'98, Springer Verlag, 639-518, 1998 (erratum)
B. Hammer, Training a sigmoidal network is difficult, in: M. Verleysen (ed.), European Symposium on Artificial Neural Networks'98, D-facto publications, 255-260, 1998
B. Hammer, On the generalization of Elman networks, in: W. Gerstner, A. Germond, M. Hasler, J.-D. Nicaud (eds.), International Conference on Artificial Neural Networks'97, Springer Verlag, 409-414, 1997
B. Hammer, V. Sperschneider Neural networks can approximate mappings on structured objects, in: P. P. Wang (ed.), International Conference on Computational Intelligence and Neural Networks'97, 211-414, 1997

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Technical reports:

B.Hammer, F.-M.Schleif, T.Villmann, On the Generalization Ability of Prototype-Based Classifiers with Local Relevance Determination, Clausthal University of Technology, Ifi-05-14, 2005.
B.Hammer, P.Tino, A.Micheli, A mathematical characterization of the architectural bias of recursive models, Osnabrücker Schriften zur Mathematik, Preprint, no.252, 5/2004.
B. Hammer, M. Strickert, T. Villmann, On the generalization ability of GRLVQ networks, Osnabrücker Schriften zur Mathematik, Preprint, no. 249, 10/2003
T. Villmann, B. Hammer, Metric adaptation and relevance learning in learning vector quantization, Osnabrücker Schriften zur Mathematik, Preprint, no. 247, 7/2003
B. Hammer, A. Micheli, and A. Sperduti, A general framework for self-organizing structure processing neural networks, Technical Report TR-03-04, Dipartimento di Informatica, Universita di Pisa, 2003.
B. Hammer, P. Tino, Neural networks with small weights implement finite memory machines, Osnabrücker Schriften zur Mathematik, Preprint, no. 241, 2/2002
B. DasGupta, B. Hammer, Hardness of approximation of the loading problem for multi-layered feedforward networks, DIMACS Technical Report #99-60, DIMACS Center, Rutgers University, 1999
B. Hammer, A NP-hardness Result for a Sigmoidal 3-Node Neural Network, Osnabrücker Schriften zur Mathematik, Preprint, no. 196, 11/1997
B. Hammer, Learning recursive data is intractable, Osnabrücker Schriften zur Mathematik, Preprint, no. 194, 7/1997
B. Hammer, On the Generalization Capability of Simple Recurrent Neural Networks, Osnabrücker Schriften zur Mathematik, Preprint, no. 190, 2/1997
B. Hammer, Universal Approximation of Mappings on Structured Objects using the Folding Architecture, Osnabrücker Schriften zur Mathematik, Preprint, no. 183, 11/1996

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Others:

B.Hammer, C.Saunders, A.Sperduti, Editorial, Introduction, Special Issue on neural networks and kernel methods for structured domains, Neural Networks, 18, 1015-1018, 2005.
M.Cottrell, B.Hammer, T.Villmann, Editorial, New Aspects in Neurocomputing, Neurocomputing 63, 1-3, 2005.
Schleif, F.-M.; Hammer, B.; Decker, J.; Clauss, U.; Villmann, Th. (2004) Automatic Classification of Mass Spectrometric Data in the Clinical Research Abstracts - 3rd Annual Leipzig Research Festival for Life Science 2004 (page 215) ISBN-3-00-015269-5
Schleif, F.-M.; Villmann, Th.; Hammer, B.; Clauss, U. (2005) Generalized Relevance Learning Vector Quantization with local Metric Adaptation in Classification of Mass Spectrometric Data Abstracts - 29th Annual Conference of the German Classification Society (GfKl 2005) (page 167)
Schleif, F.-M.; Villmann, Th.; Hammer, B.;(2005) Fuzzy Dynamic Labeling in Learning Vector Quantization for Automatic Classification of Mass Spectrometric Data in Clinical Research Abstracts - 4th Bio-Technology-Day (BioTech 2005) (page 210)
B.Hammer, Mathematical aspects of Neural Networks, 'Wrapper' around my Habilitationsschrift, Universität Osnabrück, 2003.
B. Hammer, Approximation and generalization issues of recurrent networks dealing with structured data, ECAI-2000 workshop, Foundations of connectionistic-symbolic integration: representation, paradigms, and algorithms, Berlin, 2000
B. Hammer, Neural networks classifying symbolic data, ICML-2000 workshop, Stanford, U.S.A., in L. de Raedt, S. Kramer (eds.): Proceedings of the ICML-2000 Workshop on Attribute-Value and Relational Learing: Crossing the Boundaries, 61-65, 2000
B. Hammer, Learning with Recurrent Neural Networks, Dissertation, Universität Osnabrück, 1999
B. Hammer, Die Beilinson-Spektralsequenz und Anwendungen, Diplomarbeit, Universität Osnabrück, 1995
B. Hammer, Review of Neural Smithing (Russel D. Reed, Robert J. Marks II), Pattern analysis and Applications 4, 73-74, 2001.
B. Hammer, Review of Eine Grundlegung der Average-Case Komplexitätstheorie (Ingrid Biehl), unix/mail 14(6):410, 1996

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Homepage - CI Group - Computer Science - Clausthal University of Technology

B.Hammer

Prof. Dr. Barbara Hammer

Publications