Frank-Michael Schleif

fschleif@techfak.uni-bielefeld.de
ORCID logo https://orcid.org/0000-0002-7539-1283

PEVZ-ID
19373284

133 Publikationen

Alle markieren

  • [133]
    2015 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 2759763
    Schleif, F.-M., Zhu, X., Hammer, B.: Sparse conformal prediction for dissimilarity data. Annals of Mathematics and Artificial Intelligence. 74, 95-116 (2015).
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  • [132]
    2015 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 2710031 OA
    Mokbel, B., Paaßen, B., Schleif, F.-M., Hammer, B.: Metric learning for sequences in relational LVQ. Neurocomputing. 169, 306-322 (2015).
    PUB | PDF | DOI | Download (ext.) | WoS
     
  • [131]
    2015 | Sammelwerksbeitrag | Veröffentlicht | PUB-ID: 2910885
    Schleif, F.-M., Gisbrecht, A., Tino, P.: Large Scale Indefinite Kernel Fisher Discriminant. In: Feragen, A., Pelillo, M., and Loog, M. (eds.) Similarity-Based Pattern Recognition. Similarity-Based Pattern Recognition : Third International Workshop, SIMBAD 2015, Proceedings. Lecture Notes in Computer Science. 9370, p. 160-170. Springer International Publishing, Cham (2015).
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  • [130]
    2015 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2910619
    Schleif, F.-M., Villmann, T., Zhu, X.: High Dimensional Matrix Relevance Learning. 2014 IEEE International Conference on Data Mining Workshop. IEEE, Piscataway, NJ (2015).
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  • [129]
    2015 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 2772422
    Gisbrecht, A., Schleif, F.-M.: Metric and non-metric proximity transformations at linear costs. Neurocomputing. 167, 643-657 (2015).
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  • [128]
    2014 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 2678214
    Hofmann, D., Schleif, F.-M., Paaßen, B., Hammer, B.: Learning interpretable kernelized prototype-based models. Neurocomputing. 141, 84-96 (2014).
    PUB | DOI | Download (ext.) | WoS
     
  • [127]
    2014 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 2672504
    Zhu, X., Schleif, F.-M., Hammer, B.: Adaptive Conformal Semi-Supervised Vector Quantization for Dissimilarity Data. Pattern Recognition Letters. 49, 138-145 (2014).
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  • [126]
    2014 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 2615730
    Hammer, B., Hofmann, D., Schleif, F.-M., Zhu, X.: Learning vector quantization for (dis-)similarities. NeuroComputing. 131, 43-51 (2014).
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  • [125]
    2014 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 2690490
    Strickert, M., Bunte, K., Schleif, F.-M., Huellermeier, E.: Correlation-based embedding of pairwise score data. Neurocomputing. 141, 97-109 (2014).
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  • [124]
    2013 | Sammelwerksbeitrag | Veröffentlicht | PUB-ID: 2982105
    Schleif, F.-M., Zhu, X., Hammer, B.: Sparse Prototype Representation by Core Sets. In: Yin, H., Tang, K., Gao, Y., Klawonn, F., Lee, M., Weise, T., Li, B., and Yao, X. (eds.) Intelligent Data Engineering and Automated Learning – IDEAL 2013. Lecture Notes in Computer Science. p. 302-309. Springer Berlin Heidelberg, Berlin, Heidelberg (2013).
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  • [123]
    2013 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 2612731
    Micheli, A., Schleif, F.-M., Tino, P.: Novel approaches in machine learning and computational intelligence. Neurocomputing. 112, 1-3 (2013).
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  • [122]
    2013 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2625202
    Schleif, F.-M., Zhu, X., Hammer, B.: Sparse prototype representation by core sets. In: Hujun Yin, et.al (ed.) IDEAL 2013. (2013).
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  • [121]
    2013 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2615717
    Zhu, X., Schleif, F.-M., Hammer, B.: Secure Semi-supervised Vector Quantization for Dissimilarity Data. In: Rojas, I., Joya, G., and Cabestany, J. (eds.) IWANN (1). Lecture Notes in Computer Science. 7902, p. 347-356. Springer (2013).
    PUB | DOI
     
  • [120]
    2013 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2615724
    Schleif, F.-M., Gisbrecht, A.: Data Analysis of (Non-)Metric Proximities at Linear Costs. Proceedings of SIMBAD 2013. p. 59-74. Springer, Berlin, Heidelberg (2013).
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  • [119]
    2013 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2615701
    Zhu, X., Schleif, F.-M., Hammer, B.: Semi-Supervised Vector Quantization for proximity data. Proceedings of ESANN 2013. p. 89-94. (2013).
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  • [118]
    2012 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 2625232
    Gisbrecht, A., Mokbel, B., Schleif, F.-M., Zhu, X., Hammer, B.: Linear Time Relational Prototype Based Learning. International Journal of Neural Systems. 22, : 1250021 (2012).
    PUB | DOI | WoS | PubMed | Europe PMC
     
  • [117]
    2012 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2615745
    Bunte, K., Schleif, F.-M., Biehl, M.: Adaptive Learning for complex-valued data. Proceedings of ESANN 2012. p. 387-392. (2012).
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  • [116]
    2012 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2534898
    Biehl, M., Bunte, K., Schleif, F.-M., Schneider, P., Villmann, T.: Large margin linear discriminative visualization by Matrix Relevance Learning. In: IEEE Computational Intelligence Society and Institute of Electrical and Electronics Engineers (eds.) IJCNN. p. 1-8. IEEE, Piscataway, NJ (2012).
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  • [115]
    2012 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2615750
    Schleif, F.-M., Zhu, X., Gisbrecht, A., Hammer, B.: Fast approximated relational and kernel clustering. Proceedings of ICPR 2012. p. 1229-1232. IEEE (2012).
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  • [114]
    2012 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2615756
    Schleif, F.-M., Zhu, X., Hammer, B.: Soft Competitive Learning for large data sets. Proceedings of MCSD 2012. p. 141-151. Springer Berlin Heidelberg, Berlin, Heidelberg (2012).
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  • [113]
    2012 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2534877
    Schleif, F.-M., Mokbel, B., Gisbrecht, A., Theunissen, L., Dürr, V., Hammer, B.: Learning Relevant Time Points for Time-Series Data in the Life Sciences. ICANN (2). Lecture Notes in Computer Science. 7553, p. 531-539. Springer Berlin Heidelberg, Berlin, Heidelberg (2012).
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  • [112]
    2012 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 2489405
    Bunte, K., Schneider, P., Hammer, B., Schleif, F.-M., Villmann, T., Biehl, M.: Limited Rank Matrix Learning, discriminative dimension reduction and visualization. Neural Networks. 26, 159-173 (2012).
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  • [111]
    2012 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2534888
    Schleif, F.-M., Zhu, X., Hammer, B.: A Conformal Classifier for Dissimilarity Data. AIAI (2). p. 234-243. Springer Berlin Heidelberg, Berlin, Heidelberg (2012).
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  • [110]
    2012 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2534910
    Zhu, X., Schleif, F.-M., Hammer, B.: Patch Processing for Relational Learning Vector Quantization. ISNN (1). p. 55-63. Springer Berlin Heidelberg, Berlin, Heidelberg (2012).
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  • [109]
    2012 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2534868
    Hammer, B., Mokbel, B., Schleif, F.-M., Zhu, X.: White Box Classification of Dissimilarity Data. HAIS (1). p. 309-321. Springer Berlin Heidelberg, Berlin, Heidelberg (2012).
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  • [108]
    2012 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2534905
    Schleif, F.-M., Gisbrecht, A., Hammer, B.: Relevance learning for short high-dimensional time series in the life sciences. In: IEEE Computational Intelligence Society and Institute of Electrical and Electronics Engineers (eds.) IJCNN. p. 1-8. IEEE, Piscataway, NJ (2012).
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  • [107]
    2012 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 2509852
    Zhu, X., Gisbrecht, A., Schleif, F.-M., Hammer, B.: Approximation techniques for clustering dissimilarity data. Neurocomputing. 90, 72-84 (2012).
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  • [106]
    2011 | Sammelwerksbeitrag | Veröffentlicht | PUB-ID: 2982113
    Hammer, B., Gisbrecht, A., Hasenfuss, A., Mokbel, B., Schleif, F.-M., Zhu, X.: Topographic Mapping of Dissimilarity Data. In: Laaksonen, J. and Honkela, T. (eds.) Advances in Self-Organizing Maps. Lecture Notes in Computer Science. p. 1-15. Springer Berlin Heidelberg, Berlin, Heidelberg (2011).
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  • [105]
    2011 | Sammelwerksbeitrag | Veröffentlicht | PUB-ID: 2982112
    Hammer, B., Schleif, F.-M., Zhu, X.: Relational Extensions of Learning Vector Quantization. In: Lu, B.-L., Zhang, L., and Kwok, J. (eds.) Neural Information Processing. Lecture Notes in Computer Science. p. 481-489. Springer Berlin Heidelberg, Berlin, Heidelberg (2011).
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  • [104]
    2011 | Sammelwerksbeitrag | Veröffentlicht | PUB-ID: 2982111
    Hammer, B., Mokbel, B., Schleif, F.-M., Zhu, X.: Prototype-Based Classification of Dissimilarity Data. In: Gama, J., Bradley, E., and Hollmén, J. (eds.) Advances in Intelligent Data Analysis X. Lecture Notes in Computer Science. p. 185-197. Springer Berlin Heidelberg, Berlin, Heidelberg (2011).
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  • [103]
    2011 | Sammelwerksbeitrag | Veröffentlicht | PUB-ID: 2982110
    Schleif, F.-M., Gisbrecht, A., Hammer, B.: Accelerating Kernel Neural Gas. In: Honkela, T., Duch, W., Girolami, M., and Kaski, S. (eds.) Artificial Neural Networks and Machine Learning – ICANN 2011. Lecture Notes in Computer Science. p. 150-158. Springer Berlin Heidelberg, Berlin, Heidelberg (2011).
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  • [102]
    2011 | Preprint | Veröffentlicht | PUB-ID: 2534994
    Schleif, F.-M., Gisbrecht, A., Hammer, B.: Supervised learning of short and high-dimensional temporal sequences for life science measurements. (2011).
    PUB | arXiv
     
  • [101]
    2011 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2276480
    Gisbrecht, A., Schleif, F.-M., Zhu, X., Hammer, B.: Linear time heuristics for topographic mapping of dissimilarity data. Intelligent Data Engineering and Automated Learning - IDEAL 2011: IDEAL 2011, 12th international conference, Norwich, UK, September 7 - 9, 2011 ; proceedings. Lecture Notes in Computer Science. 6936, p. 25-33. Springer, Berlin, Heidelberg (2011).
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  • [100]
    2011 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2276485
    Hammer, B., Gisbrecht, A., Hasenfuss, A., Mokbel, B., Schleif, F.-M., Zhu, X.: Topographic Mapping of Dissimilarity Data. WSOM'11. (2011).
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  • [99]
    2011 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2276492
    Schleif, F.-M., Gisbrecht, A., Hammer, B.: Accelerating Kernel Neural Gas. In: Kaski, S., Honkela, T., Gitolami, M., and Dutch, W. (eds.) ICANN'2011. (2011).
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  • [98]
    2011 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2276644
    Seiffert, U., Schleif, F.-M., Zühlke, D.: Recent Trends in Computational Intelligence in Life Science. Proceedings of ESANN 2011. p. 77-86. (2011).
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  • [97]
    2011 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2276640
    Bunte, K., Schleif, F.-M., Villmann, T.: Mathematical Foundations of the Self Organized Neighbor Embedding (SONE) for Dimension Reduction and Visualization. Proceedings of ESANN 2011. p. 29-34. Ciaco - i6doc.com (2011).
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  • [96]
    2011 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 2290045
    Lee, J.A., Schleif, F.-M., Martinetz, T.: Advances in artificial neural networks, machine learning, and computational intelligence. Neurocomputing. 74, 1299-1300 (2011).
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  • [95]
    2011 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 2309980
    Schleif, F.-M., Villmann, T., Hammer, B., Schneider, P.: Efficient Kernelized Prototype-based Classification. International Journal of Neural Systems. 21, 443-457 (2011).
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  • [94]
    2011 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2276522
    Gisbrecht, A., Hammer, B., Schleif, F.-M., Zhu, X.: Accelerating dissimilarity clustering for biomedical data analysis. IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology. p. pp.154-161. (2011).
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  • [93]
    2011 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2276654
    Schleif, F.-M.: Sparse Kernel Vector Quantization with Local Dependencies. Proceedings of IJCNN 2011. p. accepted. (2011).
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  • [92]
    2011 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 1992489
    Mwebaze, E., Schneider, P., Schleif, F.-M., Aduwo, J.R., Quinn, J.A., Haase, S., Villmann, T., Biehl, M.: Divergence based classification in Learning Vector Quantization. Neurocomputing. 74, 1429-1435 (2011).
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  • [91]
    2011 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 2094556
    Schleif, F.-M., Riemer, T., Boerner, U., Schnapka-Hille, L., Cross, M.: Genetic algorithm for shift-uncertainty correction in 1-D NMR-based metabolite identifications and quantifications. Bioinformatics. 27, 524-533 (2011).
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  • [90]
    2011 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2276636
    Schleif, F.-M., Simmuteit, S., Villmann, T.: Hierarchical deconvolution of linear mixtures of high-dimensional mass spectra in micro-biology. Proceedings of AIA 2011. p. in press. (2011).
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  • [89]
    2011 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2276648
    Schneider, P., Geweniger, T., Schleif, F.-M., Biehl, M., Villmann, T.: Multivariate class labeling in Robust Soft LVQ. Proceedings of ESANN 2011. p. 17-22. (2011).
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  • [88]
    2010 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2276626
    Simmuteit, S., Schleif, F.-M., Villmann, T.: Hierarchical evolving trees together with global and local learning for large data sets in MALDI imaging. Proceedings of WCSB 2010. p. 103-106. (2010).
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  • [87]
    2010 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 1994127
    Villmann, T., Haase, S., Schleif, F.-M., Hammer, B.: Divergence Based Online Learning in Vector Quantization. In: Rutkowski, L., Scherer, R., Tadeusiewicz, R., Zadeh, L., and Zurada, J. (eds.) Artificial Intelligence and Soft Computing. Lecture Notes in Computer Science, 6113. p. 479-486. Springer, Berlin, Heidelberg (2010).
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  • [86]
    2010 | Konferenzbeitrag | Im Druck | PUB-ID: 1992498
    Mwebaze, E., Schneider, P., Schleif, F.-M., Haase, S., Villmann, T., Biehl, M.: Divergence based Learning Vector Quantization. Proceedings of ESANN 2010. (In Press).
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  • [85]
    2010 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2276630
    Schleif, F.-M., Riemer, T., Boerner, U., Schnapka-Hille, L., Cross, M.: Efficient identification and quantification of metabolites in 1-H NMR measurements by a novel data encoding approach. Proceedings of WCSB 2010. p. 91-94. (2010).
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  • [84]
    2010 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 1992441
    Angulo, C., Lee, J.A., Schleif, F.-M.: Advances in computational intelligence and learning. NeuroComputing. 73, 1049-1050 (2010).
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  • [83]
    2010 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 1993978
    Schleif, F.-M., Villmann, T., Hammer, B., Schneider, P., Biehl, M.: Generalized derivative based Kernelized learning vector quantization. In: Fyfe, C., Tino, P., Charles, D., Garcia-Osorio, C., and Yin, H. (eds.) Intelligent Data Engineering and Automated Learning – IDEAL 2010 11th International Conference, Paisley, UK, September 1-3, 2010. Proceedings. p. 21-28. Springer, Berlin u.a. (2010).
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  • [82]
    2010 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 1994034
    Simmuteit, S., Schleif, F.-M., Villmann, T., Hammer, B.: Evolving trees for the retrieval of mass spectrometry-based bacteria fingerprints. Knowledge and Information Systems. 25, 327-343 (2010).
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  • [81]
    2010 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 1992623
    Zühlke, D., Schleif, F.-M., Geweniger, T., Villmann, T.: Learning vector quantization for heterogeneous structured data. Proceedings of the 18th European Symposium on Artificial Neural Networks (ESANN) 2010. d-side publications, Evere, Belgium (2010).
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  • [80]
    2010 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 1994138
    Villmann, T., Haase, S., Schleif, F.-M., Hammer, B., Biehl, M.: The Mathematics of Divergence Based Online Learning in Vector Quanitzation. In: El Gayar, N. and Schwenker, F. (eds.) ANNPR'2010. p. 108-119. Springer, Berlin, Heidelberg (2010).
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  • [79]
    2010 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 1994227
    Villmann, T., Schleif, F.-M., Hammer, B.: Sparse representation of data. In: Verleysen, M. (ed.) ESANN'10. p. 225-234. D side (2010).
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  • [78]
    2009 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 1993984
    Schleif, F.-M., Villmann, T., Kostrzewa, M., Hammer, B., Gammerman, A.: Cancer Informatics by Prototype-networks in Mass Spectrometry. Artificial Intelligence in Medicine. 45, 215-228 (2009).
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  • [77]
    2009 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 1992551
    Schleif, F.-M., Villmann, T.: Neural Maps and Learning Vector Quantization - Theory and Applications. Proceedings of the ESANN 2009. European Symposium on Artificial Neural Networks. Advances in Computational Intelligence and Learning. p. 509-516. d-side publications, Evere, Belgium (2009).
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  • [76]
    2009 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 1992570
    Simmuteit, S., Schleif, F.-M., Villmann, T., Kostrzewa, M.: Hierarchical PCA using Tree-SOM for the Identification of Bacteria. In: Príncipe, J.C. and Miikkulainen, R. (eds.) Advances in Self-Organizing Maps. Proceedings of the 7th International Workshop on Self Organizing Maps WSOM 2009. LNCS, 5629. p. 272-280. Springer, Berlin (2009).
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  • [75]
    2009 | Sammelwerksbeitrag | Veröffentlicht | PUB-ID: 1992575
    Simmuteit, S., Simmuteit, J., Schleif, F.-M., Villmann, T.: Deconvolution and Identification of Mass Spectra from mixed and pure colonies of bacteria. In: Blazewicz, J., Ecker, K., and Hammer, B. (eds.) ICOLE 2009. IfI-09-12. p. 104-112. Technical University of Clausthal, Clausthal-Zellerfeld, Germany (2009).
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  • [74]
    2009 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 1992607
    Villmann, T., Schleif, F.-M.: Functional Vector Quantization by Neural Maps. In: Institute of Electrical and Electronics Engineers (ed.) Proceedings of Whispers 2009. p. 636. IEEE, Piscataway, NJ (2009).
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  • [73]
    2009 | Sammelwerksbeitrag | Veröffentlicht | PUB-ID: 1994067
    Strickert, M., Schleif, F.-M., Villmann, T., Seiffert, U.: Unleashing Pearson Correlation for Faithful Analysis of Biomedical Data. In: Biehl, M., Hammer, B., Verleysen, M., and Villmann, T. (eds.) Similarity-based Clustering. LNAI, 5400. p. 70-91. Springer, Berlin (2009).
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  • [72]
    2009 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 1992547
    Schleif, F.-M., Biehl, M., Vellido, A.: Advances in machine learning and computational intelligence. NeuroComputing. 72, 1377-1378 (2009).
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  • [71]
    2009 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 1992580
    Strickert, M., Keilwagen, J., Schleif, F.-M., T. Villmann, T., Biehl, M.: Matrix metric adaptation for improved linear discriminant analysis of biomedical data. In: Cabestany, J., Sandoval, F., Prieto, A., and Corchado, J.M. (eds.) Bio-Inspired Systems: Computational and Ambient Intelligence, 10th International Work-Conference on Artificial Neural Networks, IWANN 2009, Proceedings. LNCS, 5517. Part 1, p. 933-940. Springer, Berlin (2009).
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  • [70]
    2009 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 1992526
    Schleif, F.-M., Villmann, T., Ongyerth, M.: Supervised data analysis and reliability estimation for spectral data. NeuroComputing. 72, 3590-3601 (2009).
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  • [69]
    2009 | Report | Veröffentlicht | PUB-ID: 1993316
    Biehl, M., Hammer, B., Schleif, F.-M., Schneider, P., Villmann, T.: Stationarity of Matrix Relevance Learning Vector Quantization. Machine Learning Reports. Universität Leipzig, Leipzig (2009).
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  • [68]
    2009 | Sammelwerksbeitrag | Veröffentlicht | PUB-ID: 1992534
    Schleif, F.-M., Riemer, T., Boerner, U., Cross, M.: Extended Targeted Profiling to Identify and Quantify Metabolites in 1-H NMR measurements. In: Blazewicz, J., Ecker, K., and Hammer, B. (eds.) ICOLE 2009. IfI-09-12. p. 89-103. Technical University of Clausthal, Clausthal-Zellerfeld, Germany (2009).
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  • [67]
    2009 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 1992565
    Simmuteit, S., Schleif, F.-M., Villmann, T., Elssner, T.: Tanimoto metric in Tree-SOM for improved representation of mass spectrometry data with an underlying taxonomic structure. Proceedings of ICMLA 2009. p. 563--567. IEEE Press (2009).
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  • [66]
    2009 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 1992517
    Schleif, F.-M., Lindemann, M., Maass, P., Diaz, M., Decker, J., Elssner, T., Kuhn, M., Thiele, H.: Support Vector Classification of Proteomic Profile Spectra based on Feature Extraction with the Bi-orthogonal Discrete Wavelet Transform. Computing and Visualization in Science. 12, 189-199 (2009).
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  • [65]
    2008 | Sammelwerksbeitrag | Veröffentlicht | PUB-ID: 1993939
    Schleif, F.-M., Villmann, T., Hammer, B.: Pattern Recognition by Supervised Relevance Neural Gas and its Application to Spectral Data in Bioinformatics. In: Dopico, J.R.-n R.-al, Dorado, J., and Pazos, A. (eds.) Encyclopedia of Artificial Intelligence. p. 1337-1342. IGI Global (2008).
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  • [64]
    2008 | Report | Veröffentlicht | PUB-ID: 1993379
    Bunte, K., Schneider, P., Hammer, B., Schleif, F.-M., Villmann, T., Biehl, M.: Discriminative Visualization by Limited Rank Matrix Learning. Machine Learning Reports. Universität Leipzig, Leipzig (2008).
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  • [63]
    2008 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 1992530
    Schleif, F.-M., Ongyerth, M., Villmann, T.: Sparse coding Neural Gas for analysis of Nuclear Magnetic Resonance Spectroscopy. Proceedings of the CBMS 2008. p. 620-625. IEEE (2008).
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  • [62]
    2008 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 1992554
    Schneider, P., Schleif, F.-M., Villmann, T., Biehl, M.: Generalized Matrix Learning Vector Quantizer for the Analysis of Spectral Data. In: Verleysen, M. (ed.) Proceedings of the 16th European Symposium on Artificial Neural Networks (ESANN) 2008. p. 451-456. d-side publications, Evere, Belgium (2008).
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  • [61]
    2008 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 1992597
    Strickert, M., Schleif, F.-M., Villmann, T.: Metric adaptation for supervised attribute rating. In: Verleysen, M. (ed.) Proceedings of the 16th European Symposium on Artificial Neural Networks (ESANN) 2008. p. 31-36. d-side publications, Evere, Belgium (2008).
    PUB
     
  • [60]
    2008 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 1993966
    Schleif, F.-M., Villmann, T., Hammer, B.: Prototype based Fuzzy Classification in Clinical Proteomics. International Journal of Approximate Reasoning. 47, 4-16 (2008).
    PUB | DOI | WoS
     
  • [59]
    2008 | Sammelwerksbeitrag | Veröffentlicht | PUB-ID: 1993900
    Schleif, F.-M., Hammer, B., Villmann, T.: Analysis of Spectral Data in Clinical Proteomics by use of Learning Vector Quantizers. In: Van de Werff, M., Delder, A., and Tollenaar, R. (eds.) Computational Intelligence in Biomedicine and Bioinformatics: Current Trends and Applications. p. 141-167. Springer, Berlin (2008).
    PUB | DOI
     
  • [58]
    2008 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 1992539
    Schleif, F.-M., Riemer, T., Cross, M., Villmann, T.: Automatic Identification and Quantification of Metabolites in H-NMR Measurements. Proceedings of the Workshop on Computational Systems Biology (WCSB) 2008. p. 165-168. (2008).
    PUB
     
  • [57]
    2008 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 1992589
    Strickert, M., Schleif, F.-M., Seiffert, U.: Derivatives of Pearson Correlation for Gradient-based Analysis of Biomedical Data. Ibero-American Journal of Artificial Intelligence. 37, 37-44 (2008).
    PUB
     
  • [56]
    2008 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 1994253
    Villmann, T., Schleif, F.-M., Kostrzewa, M., Walch, A., Hammer, B.: Classification of mass-spectrometric data in clinical proteomics using learning vector quantization methods. Briefings in Bioinformatics. 9, 129-143 (2008).
    PUB | DOI | WoS | PubMed | Europe PMC
     
  • [55]
    2008 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 2017617
    Villmann, T., Hammer, B., Schleif, F.-M., Hermann, W., Cottrell, M.: Fuzzy Classification Using Information Theoretic Learning Vector Quantization. Neurocomputing. 71, 3070-3076 (2008).
    PUB | DOI | WoS
     
  • [54]
    2008 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2001836
    Geweniger, T., Schleif, F.-M., Hasenfuss, A., Hammer, B., Villmann, T.: Comparison of cluster algorithms for the analysis of text data using Kolmogorov complexity. In: Köppen, M., Kasabov, N.K., and Coghill, G.G. (eds.) ICONIP 2008. p. 61-69. Springer, Berlin, Heidelberg (2008).
    PUB | DOI
     
  • [53]
    2007 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 1994016 OA
    Schneider, P., Biehl, M., Schleif, F.-M., Hammer, B.: Advanced metric adaptation in Generalized LVQ for classification of mass spectrometry data. Proceedings of 6th International Workshop on Self-Organizing Maps (WSOM 2007). Bielefeld University, Bielefeld (2007).
    PUB | PDF | DOI
     
  • [52]
    2007 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 1994267 OA
    Villmann, T., Schleif, F.-M., Merenyi, E., Strickert, M., Hammer, B.: Class imaging of hyperspectral satellite remote sensing data using FLSOM. Proceedings of 6th International Workshop on Self-Organizing Maps (WSOM 2007). Bielefeld University, Bielefeld (2007).
    PUB | PDF | DOI
     
  • [51]
    2007 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 1993547
    Hammer, B., Hasenfuss, A., Schleif, F.-M., Villmann, T., Strickert, M., Seiffert, U.: Intuitive Clustering of Biological Data. Proceedings of International Joint Conference on Neural Networks. p. 1877-1882. IEEE (2007).
    PUB | DOI
     
  • [50]
    2007 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 1993852
    Schleif, F.-M.: Advances in pre-processing and model generation for mass spectrometric data analysis. In: Biehl, M., Hammer, B., Verleysen, M., and Villmann, T. (eds.) Similarity-based Clustering and its Application to Medicine and Biology. Dagstuhl Seminar Proceedings. Internationales Begegnungs- und Forschungszentrum für Informatik (IBFI), Schloss Dagstuhl, Germany, Dagstuhl, Germany (2007).
    PUB
     
  • [49]
    2007 | Report | Veröffentlicht | PUB-ID: 1993922
    Schleif, F.-M., Hasenfuss, A., Hammer, B.: Aggregation of multiple peak lists by use of an improved neural gas network. Universität Leipzig, Leipzig (2007).
    PUB
     
  • [48]
    2007 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 1992602
    Strickert, M., Schleif, F.-M., Villmann, T., Seiffert, U.: Derivatives of Pearson Correlation for Gradient based Analysis of Biomedical Data. Similarity based Clustering. Lecture Notes in Artificial Intelligence, 5400. 12, IBERAMIA: Sociedad Iberoamericana de Inteligencia Artificial (2007).
    PUB | DOI
     
  • [47]
    2007 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 1993970
    Schleif, F.-M., Villmann, T., Hammer, B.: Analysis of Proteomic Spectral Data by Multi Resolution Analysis and Self-Organizing-Maps. In: Masulli, F., Mitra, S., and Pasi, G. (eds.) Application of Fuzzy Sets Theory. Proceedings of the 7th International Workshop on Fuzzy Logic and Applications. LNAI 4578. p. 563-570. Springer, Berlin, Heidelberg (2007).
    PUB | DOI
     
  • [46]
    2007 | Sammelwerksbeitrag | Veröffentlicht | PUB-ID: 1992509
    Schleif, F.-M.: Prototypen basiertes maschinelles Lernen in der klinischen Proteomik. In: Wagner, D. (ed.) Ausgezeichnete Informatikdissertationen 2006. GI-Edition Lecture Notes in Informatics. Dissertation. 7, p. 179-188. Gesellschaft für Informatik, Bonn (2007).
    PUB
     
  • [45]
    2007 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 1993911
    Schleif, F.-M., Hammer, B., Villmann, T.: Margin based Active Learning for LVQ Networks. Neurocomputing. 70, 1215-1224 (2007).
    PUB | DOI | WoS
     
  • [44]
    2007 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 1992507
    Schleif, F.-M.: Maschinelles Lernen mit Prototypmethoden in der klinischen Proteomik. KI - Künstliche Intelligenz. 65-67 (2007).
    PUB
     
  • [43]
    2007 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 1992610
    Villmann, T., Schleif, F.-M., v.d.Werff, M., Deelder, A., Tollenaar, R.: Association learning in SOMs for Fuzzy-Classification. 6th International Conference on Machine Learning and Applications, 2007. p. 581-586. (2007).
    PUB | DOI
     
  • [42]
    2007 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 1993811
    Hasenfuss, A., Hammer, B., Schleif, F.-M., Villmann, T.: Neural gas clustering for dissimilarity data with continuous prototypes. In: Sandoval, F., Prieto, A., Cabestany, J., and Grana, M. (eds.) Computational and Ambient Intelligence – Proceedings of the 9th Work-conference on Artificial Neural Networks. LNCS 4507. p. 539-546. Springer, Berlin (2007).
    PUB | DOI
     
  • [41]
    2007 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 1992616
    Villmann, T., Strickert, M., Brüß, C., Schleif, F.-M., Seiffert, U.: Visualization of fuzzy information in in fuzzy-classification for image sagmentation using MDS. In: Verleysen, M. (ed.) Proceedings of the 15th European Symposium on Artificial Neural Networks (ESANN) 2007. p. 103-108. d-side publications, Evere, Belgium (2007).
    PUB
     
  • [40]
    2007 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 1992452
    Deininger, S.-O., Gerhard, M., Schleif, F.-M.: Statistical Classification and Visualization of MALDI-Imaging Data. Proc. of CBMS 2007. p. 403-405. (2007).
    PUB
     
  • [39]
    2007 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 1994258
    Villmann, T., Schleif, F.-M., Merenyi, E., Hammer, B.: Fuzzy Labeled Self Organizing Map for Clasification of Spectra. In: Sandoval, F., Prieto, A., Cabestany, J., and Grana, M. (eds.) Computational and Ambient Intelligence. Proceedings of the 9th Work-conference on Artificial Neural Networks. LNCS, 4507. p. 556-563. Springer, Berlin (2007).
    PUB | DOI
     
  • [38]
    2007 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 1993820
    Hasenfuss, A., Hammer, B., Schleif, F.-M., Villmann, T.: Neural gas clustering for sparse proximity data. In: Sandoval, F., Prieto, A., Cabestany, J., and Grana, M. (eds.) Proceedings of the 9th International Work-Conference on Artificial Neural Networks.LNCS 4507. p. 539-546. Springer, Berlin, Heidelberg, Germany (2007).
    PUB
     
  • [37]
    2007 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 1993907
    Schleif, F.-M., Hammer, B., Villmann, T.: Supervised Neural Gas for Functional Data and its Application to the Analysis of Clinical Proteom Spectra. In: Sandoval, F., Prieto, A., Cabestany, J., and Grana, M. (eds.) Computational and Ambient Intelligence. Proceedings of the 9th International Work-Conference on Artificial Neural Networks. LNCS, 4507. p. 1036-1044. Springer, Berlin, Heidelberg (2007).
    PUB | DOI
     
  • [36]
    2007 | Report | Veröffentlicht | PUB-ID: 1992505
    Schleif, F.-M.: Preprocessing of Nuclear Magnetic Resonance Spectrometry Data. Machine Learning Reports. Universität Leipzig, Leipzig (2007).
    PUB
     
  • [35]
    2007 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 1992586
    Strickert, M., Schleif, F.-M.: Supervised Attribute Relevance Determination for Protein Identification in Stress Experiments. Proc. of MLSB 2007. p. 81-86. (2007).
    PUB
     
  • [34]
    2007 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 1992593
    Strickert, M., Schleif, F.-M., Seiffert, U.: Gradients of Pearson Correlation for Analysis of Biomedical Data. Proc. of ASAI 2007. p. 139-150. (2007).
    PUB
     
  • [33]
    2006 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 1994184
    Villmann, T., Hammer, B., Schleif, F.-M., Geweniger, T., Fischer, T., Cottrell, M.: Prototype based classification using information theoretic learning. In: King, I., Wang, J., Chan, L., and Wang, D.L.L. (eds.) Neural Information Processing, 13th International Conference. Proceedings. Lecture Notes in Computer Science, 4233. Part II, p. 40-49. Springer, Berlin (2006).
    PUB
     
  • [32]
    2006 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 1994273
    Villmann, T., Seiffert, U., Schleif, F.-M., Brüß, C., Geweniger, T., Hammer, B.: Fuzzy Labeled Self-Organizing Map with Label-Adjusted Prototypes. In: Schwenker, F. (ed.) Proceedings of Conference Artificial Neural Networks in Pattern Recognition. p. 46-56. Springer, Berlin (2006).
    PUB | DOI
     
  • [31]
    2006 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 1993578
    Hammer, B., Hasenfuss, A., Schleif, F.-M., Villmann, T.: Supervised Batch Neural Gas. In: Schwenker, F. (ed.) Proceedings of Conference Artificial Neural Networks in Pattern Recognition (ANNPR). p. 33-45. Springer Verlag, Berlin (2006).
    PUB | DOI
     
  • [30]
    2006 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 1993895
    Schleif, F.-M., Hammer, B., Villmann, T.: Margin based Active Learning for LVQ Networks. In: Verleysen, M. (ed.) Proc. Of European Symposium on Artificial Neural Networks. p. 539-544. d-side publications, Brussels, Belgium (2006).
    PUB
     
  • [29]
    2006 | Dissertation | PUB-ID: 1992511
    Schleif, F.-M.: Prototype based Machine Learning for Clinical Proteomics. Technical University Clausthal, Clausthal-Zellerfeld, Germany (2006).
    PUB
     
  • [28]
    2006 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 1993889
    Schleif, F.-M., Elssner, T., Kostrzewa, M., Villmann, T., Hammer, B.: Machine Learning and Soft-Computing in Bioinformatics. A Short Journey. Proc. of FLINS 2006. p. 541-548. World Scientific Press (2006).
    PUB
     
  • [27]
    2006 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 1994237
    Villmann, T., Schleif, F.-M., Hammer, B.: Comparison of relevance learning vector quantization with other metric adaptive classification methods. Neural Networks. 19, 610-622 (2006).
    PUB | DOI | WoS | PubMed | Europe PMC
     
  • [26]
    2006 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 1993568
    Hammer, B., Hasenfuss, A., Schleif, F.-M., Villmann, T.: Supervised median neural gas. In: Dagli, C., Buczak, A., Enke, D., Embrechts, A., and Ersoy, O. (eds.) Smart Engineering System Design. Intelligent Engineering Systems Through Artificial Neural Networks, 16. p. 623-633. ASME Press (2006).
    PUB
     
  • [25]
    2006 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 1993594
    Hammer, B., Hasenfuss, A., Schleif, F.-M., Villmann, T.: Supervised median clustering. In: Dagli, C.H. (ed.) Smart systems engineering : infra-structure systems engineering, bio-informatics and computational biology and evolutionary computation : proceedings of the Artificial Neural Networks in Engineering Conference (ANNIE 2006). ASME Press series on intelligent engineering systems through artificial neural networks, 16. p. 623-632. ASME Press, New York, NY (2006).
    PUB
     
  • [24]
    2006 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 1993878
    Schleif, F.-M., Elssner, T., Kostrzewa, M., Villmann, T., Hammer, B.: Analysis and Visualization of Proteomic Data by Fuzzy labeled Self-Organizing Maps. In: Lee, D.J., Nutter, B., Antani, S., Mitra, S., and Archibald, J. (eds.) 19th IEEE International Symposium on Computer- based Medical Systems. p. 919-924. IEEE Computer Society Press, Los Alamitos (2006).
    PUB | DOI
     
  • [23]
    2006 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 1992445
    Brüß, C., Bollenbeck, F., Schleif, F.-M., Weschke, W., Villmann, T., Seiffert, U.: Fuzzy Image Segmentation with Fuzzy Labelled Neural Gas. Proc. of ESANN 2006. p. 563-569. (2006).
    PUB
     
  • [22]
    2006 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 1994195
    Villmann, T., Hammer, B., Schleif, F.-M., Geweniger, T., Herrmann, W.: Fuzzy Classification by Fuzzy Labeled Neural Gas. Neural Networks. 19, 772-779 (2006).
    PUB | DOI | WoS | PubMed | Europe PMC
     
  • [21]
    2006 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 1994241
    Villmann, T., Schleif, F.-M., Hammer, B.: Prototype-based fuzzy classification with local relevance for proteomics. Neurocomputing. 69, 2425-2428 (2006).
    PUB | DOI | WoS
     
  • [20]
    2006 | Report | Veröffentlicht | PUB-ID: 1993584
    Hammer, B., Hasenfuss, A., Schleif, F.-M., Villmann, T.: Supervised median clustering. IfI Technical reports. Clausthal University of Technology, Clausthal-Zellerfeld (2006).
    PUB
     
  • [19]
    2006 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2017225
    Hammer, B., Villmann, T., Schleif, F.-M., Albani, C., Hermann, W.: Learning vector quantization classification with local relevance determination for medical data. In: Rutkowski, L., Tadeusiewicz, R., Zadeh, L.A., and Zurada, J. (eds.) Artificial Intelligence and Soft-Computing - Proceedings of ICAISC 2006. LNAI, 4029. Lecture notes in computer science ; 4029 : Lecture notes in artificial intelligence. 4029, p. 603-612. Springer, Berlin, Heidelberg (2006).
    PUB | DOI
     
  • [18]
    2005 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 1994172
    Villmann, T., Hammer, B., Schleif, F.-M., Geweniger, T.: Fuzzy Labeled Neural GAS for Fuzzy Classification. In: Cottrell, M. (ed.) Proceedings of the 5th Workshop on Self-Organizing Maps [on CD-ROM]. p. 283-290. University Paris-1-Pantheon-Sorbonne, Paris, France (2005).
    PUB
     
  • [17]
    2005 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 1992513
    Schleif, F.-M.: Plugins mit wxWidgets. Offene Systeme. 2005, 5-10 (2005).
    PUB
     
  • [16]
    2005 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 1994219
    Villmann, T., Schleif, F.-M., Hammer, B.: Fuzzy Classification for Classification of Mass Spectrometric Data Based on Learning Vector Quantization. International Workshop on Integrative Bioinformatics. (2005).
    PUB
     
  • [15]
    2005 | Report | Veröffentlicht | PUB-ID: 1993675
    Hammer, B., Schleif, F.-M., Villmann, T.: On the Generalization Ability of Prototype-Based Classifiers with Local Relevance Determination. IfI Technical reports. Clausthal University of Technology, Clausthal-Zellerfeld (2005).
    PUB
     
  • [14]
    2005 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 1994249
    Villmann, T., Schleif, F.-M., Hammer, B.: Fuzzy labeled soft nearest neighbor classification with relevance learning. In: Wani, M.A., Cios, K.J., and Hafeez, K. (eds.) Proceedings of the International Conference of Machine Learning Applications. p. 11-15. IEEE Press, Los Angeles (2005).
    PUB
     
  • [13]
    2005 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 1993974
    Schleif, F.-M., Villmann, T., Hammer, B.: Local Metric Adaptation for Soft Nearest Prototype Classification to Classify Proteomic Data. In: Bloch, I., Petrosino, A., and Tettamanzi, A.G.B. (eds.) Proceedings of the 6th Workshop on Fuzzy Logic and Applications. p. 290-296. Springer, Berlin, Heidelberg (2005).
    PUB | DOI
     
  • [12]
    2004 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 1994168
    Villmann, T., Hammer, B., Schleif, F.-M.: Metrik Adaptation for Optimal Feature Classification in Learning Vector Quantization Applied to Environment Detection. In: Groß, H.-M., Debes, K., and Böhme, H.-J. (eds.) Proceedings of Selbstorganisation Von Adaptivem Verfahren. Fortschritts-Berichte VDI Reihe 10, Nr. 742. p. 592-597. VDI Verlag (2004).
    PUB
     
  • [11]
    2004 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 1994212
    Villmann, T., Schleif, F.-M., Hammer, B.: Metric adaptation for optimal feature classification in learning vector quantization applied to environment detection. In: Groß, H.-M., Debes, K., and Böhme, H.-J. (eds.) SOAVE 2004, 3rd Workshop on SelfOrganization of AdaptiVE Behavior. VDI Verlag (2004).
    PUB
     
  • [10]
    2004 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 1993870
    Schleif, F.-M., Clauss, U., Villmann, T., Hammer, B.: Supervised Relevance Neural Gas and Unified Maximum Separability Analysis for Classification of Mass Spectrometric Data. In: Wani, M.A., Cios, K.J., and Hafeez, K. (eds.) Proceedings of the 3rd International Conference on Machine Learning and Applications (ICMLA) 2004. p. 374-379. IEEE Press, Los Alamitos, CA, USA (2004).
    PUB
     
  • [9]
    2003 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 1994223
    Villmann, T., Schleif, F.-M., Hammer, B.: Supervised Neural Gas and Relevance Learning in Learning Vector Quantization. In: Yamakawa, T. (ed.) Proceedings of the 4th Workshop on Self Organizing Maps [on CD-ROM]. p. 47-52. Kyushu Institute of Technology, Hibikino, Kitakyushu, Japan (2003).
    PUB
     
  • [8]
    2003 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 1992477
    Köhler, M., Buchta, K., Schleif, F.-M., Sommerfeld, E.: A mission for the EEG coherence analysis: Is the task complex or difficult? Brain Topography. 15, 271 (2003).
    PUB
     
  • [7]
    2003 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 1992456
    Dörfler, T., Simmel, A., Schleif, F.-M., Sommerfeld, E.: Working memory load and EEG coherence. Brain Topography. 15, 269 (2003).
    PUB
     
  • [6]
    2003 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 1992466
    Gruhn, V., Hülder, M., Ijoui, R., Schleif, F.-M.: A distributed logistic support communication system. In: Linger, H., Fisher, J., Wojtkowski, W.G., Zupancic, J., Vigo, K., and Arnold, J. (eds.) Proceedings of ISD 2003 - Constructing the Infrastructure for the Knowledge Economy - Methods and Tools, Theory and Practice. p. 705-713. Kluwer Academic Publishers, London (2003).
    PUB
     
  • [5]
    2002 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 1992544
    Schleif, F.-M., Stamer, H.: {LaTeX} im studentischen Alltag. Gaotenblatt. 3-10 (2002).
    PUB
     
  • [4]
    2002 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 1992483
    Köhler, M., Buchta, K., Schleif, F.-M., Sommerfeld, E.: Complexity and difficulty in memory based comparison. In: da Silva, J.A., Filho, N.P.R., and Matsushima, E.H. (eds.) Proceedings of the 18th Meeting of the International Society for Psychophysics. p. 433-439. Pabst Publishing (2002).
    PUB
     
  • [3]
    2002 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 1992515
    Schleif, F.-M.: OCR mit statistischen Momenten. Gaotenblatt. 2002, 15-17 (2002).
    PUB
     
  • [2]
    2001 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 1992560
    Simmel, A., Dörfler, T., Schleif, F.-M., Sommerfeld, E.: An analysis of connections between internal and external learning process indicators using EEG coherence analysis. Proceedings of the 17th Meeting of the International Society for Psychophysics. p. 602-607. Pabst Publishing (2001).
    PUB
     
  • [1]
    2001 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 1992461
    Dörfler, T., Simmel, A., Schleif, F.-M., Sommerfeld, E.: Complexity - dependent synchronization of brain subsystems during memorization. Proceedings of the 17th Meeting of the International Society for Psychophysics. p. 343-348. Pabst Publishing (2001).
    PUB
     
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