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., 2015. Sparse conformal prediction for dissimilarity data. Annals of Mathematics and Artificial Intelligence, 74(1-2), p 95-116.
    PUB | DOI | WoS
     
  • [132]
    2015 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 2710031 OA
    Mokbel, B., et al., 2015. Metric learning for sequences in relational LVQ. Neurocomputing, 169(SI), p 306-322.
    PUB | PDF | DOI | Download (ext.) | WoS
     
  • [131]
    2015 | Sammelwerksbeitrag | Veröffentlicht | PUB-ID: 2910885
    Schleif, F.-M., Gisbrecht, A., & Tino, P., 2015. Large Scale Indefinite Kernel Fisher Discriminant. In A. Feragen, M. Pelillo, & M. Loog, eds. Similarity-Based Pattern Recognition. Similarity-Based Pattern Recognition : Third International Workshop, SIMBAD 2015, Proceedings. Lecture Notes in Computer Science. no.9370 Cham: Springer International Publishing, pp. 160-170.
    PUB | DOI
     
  • [130]
    2015 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2910619
    Schleif, F.-M., Villmann, T., & Zhu, X., 2015. High Dimensional Matrix Relevance Learning. In 2014 IEEE International Conference on Data Mining Workshop. Piscataway, NJ: IEEE.
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  • [129]
    2015 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 2772422
    Gisbrecht, A., & Schleif, F.-M., 2015. Metric and non-metric proximity transformations at linear costs. Neurocomputing, 167, p 643-657.
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  • [128]
    2014 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 2678214
    Hofmann, D., et al., 2014. Learning interpretable kernelized prototype-based models. Neurocomputing, 141, p 84-96.
    PUB | DOI | Download (ext.) | WoS
     
  • [127]
    2014 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 2672504
    Zhu, X., Schleif, F.-M., & Hammer, B., 2014. Adaptive Conformal Semi-Supervised Vector Quantization for Dissimilarity Data. Pattern Recognition Letters, 49, p 138-145.
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  • [126]
    2014 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 2615730
    Hammer, B., et al., 2014. Learning vector quantization for (dis-)similarities. NeuroComputing, 131, p 43-51.
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  • [125]
    2014 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 2690490
    Strickert, M., et al., 2014. Correlation-based embedding of pairwise score data. Neurocomputing, 141, p 97-109.
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  • [124]
    2013 | Sammelwerksbeitrag | Veröffentlicht | PUB-ID: 2982105
    Schleif, F.-M., Zhu, X., & Hammer, B., 2013. Sparse Prototype Representation by Core Sets. In H. Yin, et al., eds. Intelligent Data Engineering and Automated Learning – IDEAL 2013. Lecture Notes in Computer Science. Berlin, Heidelberg: Springer Berlin Heidelberg, pp. 302-309.
    PUB | DOI
     
  • [123]
    2013 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 2612731
    Micheli, A., Schleif, F.-M., & Tino, P., 2013. Novel approaches in machine learning and computational intelligence. Neurocomputing, 112, p 1-3.
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  • [122]
    2013 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2625202
    Schleif, F.-M., Zhu, X., & Hammer, B., 2013. Sparse prototype representation by core sets. In et.al Hujun Yin, ed. IDEAL 2013.
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  • [121]
    2013 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2615717
    Zhu, X., Schleif, F.-M., & Hammer, B., 2013. Secure Semi-supervised Vector Quantization for Dissimilarity Data. In I. Rojas, G. Joya, & J. Cabestany, eds. IWANN (1). Lecture Notes in Computer Science. no.7902 Springer, pp. 347-356.
    PUB | DOI
     
  • [120]
    2013 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2615724
    Schleif, F.-M., & Gisbrecht, A., 2013. Data Analysis of (Non-)Metric Proximities at Linear Costs. In Proceedings of SIMBAD 2013. Berlin, Heidelberg: Springer, pp. 59-74.
    PUB | DOI
     
  • [119]
    2013 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2615701
    Zhu, X., Schleif, F.-M., & Hammer, B., 2013. Semi-Supervised Vector Quantization for proximity data. In Proceedings of ESANN 2013. pp. 89-94.
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  • [118]
    2012 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 2625232
    Gisbrecht, A., et al., 2012. Linear Time Relational Prototype Based Learning. International Journal of Neural Systems, 22(05): 1250021.
    PUB | DOI | WoS | PubMed | Europe PMC
     
  • [117]
    2012 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2615745
    Bunte, K., Schleif, F.-M., & Biehl, M., 2012. Adaptive Learning for complex-valued data. In Proceedings of ESANN 2012. pp. 387-392.
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  • [116]
    2012 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2534898
    Biehl, M., et al., 2012. Large margin linear discriminative visualization by Matrix Relevance Learning. In IEEE Computational Intelligence Society & Institute of Electrical and Electronics Engineers, eds. IJCNN. Piscataway, NJ: IEEE, pp. 1-8.
    PUB | DOI
     
  • [115]
    2012 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2615750
    Schleif, F.-M., et al., 2012. Fast approximated relational and kernel clustering. In Proceedings of ICPR 2012. IEEE, pp. 1229-1232.
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  • [114]
    2012 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2615756
    Schleif, F.-M., Zhu, X., & Hammer, B., 2012. Soft Competitive Learning for large data sets. In Proceedings of MCSD 2012. Berlin, Heidelberg: Springer Berlin Heidelberg, pp. 141-151.
    PUB | DOI
     
  • [113]
    2012 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2534877
    Schleif, F.-M., et al., 2012. Learning Relevant Time Points for Time-Series Data in the Life Sciences. In ICANN (2). Lecture Notes in Computer Science. no.7553 Berlin, Heidelberg: Springer Berlin Heidelberg, pp. 531-539.
    PUB | DOI
     
  • [112]
    2012 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 2489405
    Bunte, K., et al., 2012. Limited Rank Matrix Learning, discriminative dimension reduction and visualization. Neural Networks, 26, p 159-173.
    PUB | DOI | WoS | PubMed | Europe PMC
     
  • [111]
    2012 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2534888
    Schleif, F.-M., Zhu, X., & Hammer, B., 2012. A Conformal Classifier for Dissimilarity Data. In AIAI (2). Berlin, Heidelberg: Springer Berlin Heidelberg, pp. 234-243.
    PUB | DOI
     
  • [110]
    2012 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2534910
    Zhu, X., Schleif, F.-M., & Hammer, B., 2012. Patch Processing for Relational Learning Vector Quantization. In ISNN (1). Berlin, Heidelberg: Springer Berlin Heidelberg, pp. 55-63.
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  • [109]
    2012 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2534868
    Hammer, B., et al., 2012. White Box Classification of Dissimilarity Data. In HAIS (1). Berlin, Heidelberg: Springer Berlin Heidelberg, pp. 309-321.
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  • [108]
    2012 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2534905
    Schleif, F.-M., Gisbrecht, A., & Hammer, B., 2012. Relevance learning for short high-dimensional time series in the life sciences. In IEEE Computational Intelligence Society & Institute of Electrical and Electronics Engineers, eds. IJCNN. Piscataway, NJ: IEEE, pp. 1-8.
    PUB | DOI
     
  • [107]
    2012 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 2509852
    Zhu, X., et al., 2012. Approximation techniques for clustering dissimilarity data. Neurocomputing, 90, p 72-84.
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  • [106]
    2011 | Sammelwerksbeitrag | Veröffentlicht | PUB-ID: 2982113
    Hammer, B., et al., 2011. Topographic Mapping of Dissimilarity Data. In J. Laaksonen & T. Honkela, eds. Advances in Self-Organizing Maps. Lecture Notes in Computer Science. Berlin, Heidelberg: Springer Berlin Heidelberg, pp. 1-15.
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  • [105]
    2011 | Sammelwerksbeitrag | Veröffentlicht | PUB-ID: 2982112
    Hammer, B., Schleif, F.-M., & Zhu, X., 2011. Relational Extensions of Learning Vector Quantization. In B. - L. Lu, L. Zhang, & J. Kwok, eds. Neural Information Processing. Lecture Notes in Computer Science. Berlin, Heidelberg: Springer Berlin Heidelberg, pp. 481-489.
    PUB | DOI
     
  • [104]
    2011 | Sammelwerksbeitrag | Veröffentlicht | PUB-ID: 2982111
    Hammer, B., et al., 2011. Prototype-Based Classification of Dissimilarity Data. In J. Gama, E. Bradley, & J. Hollmén, eds. Advances in Intelligent Data Analysis X. Lecture Notes in Computer Science. Berlin, Heidelberg: Springer Berlin Heidelberg, pp. 185-197.
    PUB | DOI
     
  • [103]
    2011 | Sammelwerksbeitrag | Veröffentlicht | PUB-ID: 2982110
    Schleif, F.-M., Gisbrecht, A., & Hammer, B., 2011. Accelerating Kernel Neural Gas. In T. Honkela, et al., eds. Artificial Neural Networks and Machine Learning – ICANN 2011. Lecture Notes in Computer Science. Berlin, Heidelberg: Springer Berlin Heidelberg, pp. 150-158.
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  • [102]
    2011 | Preprint | Veröffentlicht | PUB-ID: 2534994
    Schleif, F.-M., Gisbrecht, A., & Hammer, B., 2011. Supervised learning of short and high-dimensional temporal sequences for life science measurements.
    PUB | arXiv
     
  • [101]
    2011 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2276480
    Gisbrecht, A., et al., 2011. Linear time heuristics for topographic mapping of dissimilarity data. In 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. no.6936 Berlin, Heidelberg: Springer, pp. 25-33.
    PUB | DOI
     
  • [100]
    2011 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2276485
    Hammer, B., et al., 2011. Topographic Mapping of Dissimilarity Data. In WSOM'11.
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  • [99]
    2011 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2276492
    Schleif, F.-M., Gisbrecht, A., & Hammer, B., 2011. Accelerating Kernel Neural Gas. In S. Kaski, et al., eds. ICANN'2011.
    PUB
     
  • [98]
    2011 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2276644
    Seiffert, U., Schleif, F.-M., & Zühlke, D., 2011. Recent Trends in Computational Intelligence in Life Science. In Proceedings of ESANN 2011. pp. 77-86.
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  • [97]
    2011 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2276640
    Bunte, K., Schleif, F.-M., & Villmann, T., 2011. Mathematical Foundations of the Self Organized Neighbor Embedding (SONE) for Dimension Reduction and Visualization. In Proceedings of ESANN 2011. Ciaco - i6doc.com, pp. 29-34.
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  • [96]
    2011 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 2290045
    Lee, J.A., Schleif, F.-M., & Martinetz, T., 2011. Advances in artificial neural networks, machine learning, and computational intelligence. Neurocomputing, 74(9), p 1299-1300.
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  • [95]
    2011 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 2309980
    Schleif, F.-M., et al., 2011. Efficient Kernelized Prototype-based Classification. International Journal of Neural Systems, 21(06), p 443-457.
    PUB | DOI | WoS | PubMed | Europe PMC
     
  • [94]
    2011 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2276522
    Gisbrecht, A., et al., 2011. Accelerating dissimilarity clustering for biomedical data analysis. In IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology. pp. pp.154-161.
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  • [93]
    2011 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2276654
    Schleif, F.-M., 2011. Sparse Kernel Vector Quantization with Local Dependencies. In Proceedings of IJCNN 2011. pp. accepted.
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  • [92]
    2011 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 1992489
    Mwebaze, E., et al., 2011. Divergence based classification in Learning Vector Quantization. Neurocomputing, 74(9), p 1429-1435.
    PUB | DOI | WoS
     
  • [91]
    2011 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 2094556
    Schleif, F.-M., et al., 2011. Genetic algorithm for shift-uncertainty correction in 1-D NMR-based metabolite identifications and quantifications. Bioinformatics, 27(4), p 524-533.
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  • [90]
    2011 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2276636
    Schleif, F.-M., Simmuteit, S., & Villmann, T., 2011. Hierarchical deconvolution of linear mixtures of high-dimensional mass spectra in micro-biology. In Proceedings of AIA 2011. pp. in press.
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  • [89]
    2011 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2276648
    Schneider, P., et al., 2011. Multivariate class labeling in Robust Soft LVQ. In Proceedings of ESANN 2011. pp. 17-22.
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  • [88]
    2010 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2276626
    Simmuteit, S., Schleif, F.-M., & Villmann, T., 2010. Hierarchical evolving trees together with global and local learning for large data sets in MALDI imaging. In Proceedings of WCSB 2010. pp. 103-106.
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  • [87]
    2010 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 1994127
    Villmann, T., et al., 2010. Divergence Based Online Learning in Vector Quantization. In L. Rutkowski, et al., eds. Artificial Intelligence and Soft Computing. Lecture Notes in Computer Science, 6113. Berlin, Heidelberg: Springer, pp. 479-486.
    PUB | DOI
     
  • [86]
    2010 | Konferenzbeitrag | Im Druck | PUB-ID: 1992498
    Mwebaze, E., et al., In Press. Divergence based Learning Vector Quantization. In Proceedings of ESANN 2010.
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  • [85]
    2010 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2276630
    Schleif, F.-M., et al., 2010. Efficient identification and quantification of metabolites in 1-H NMR measurements by a novel data encoding approach. In Proceedings of WCSB 2010. pp. 91-94.
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  • [84]
    2010 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 1992441
    Angulo, C., Lee, J.A., & Schleif, F.-M., 2010. Advances in computational intelligence and learning. NeuroComputing, 73(7-9), p 1049-1050.
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  • [83]
    2010 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 1993978
    Schleif, F.-M., et al., 2010. Generalized derivative based Kernelized learning vector quantization. In C. Fyfe, et al., eds. Intelligent Data Engineering and Automated Learning – IDEAL 2010 11th International Conference, Paisley, UK, September 1-3, 2010. Proceedings. Berlin u.a.: Springer, pp. 21-28.
    PUB | DOI
     
  • [82]
    2010 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 1994034
    Simmuteit, S., et al., 2010. Evolving trees for the retrieval of mass spectrometry-based bacteria fingerprints. Knowledge and Information Systems, 25(2), p 327-343.
    PUB | DOI | WoS
     
  • [81]
    2010 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 1992623
    Zühlke, D., et al., 2010. Learning vector quantization for heterogeneous structured data. In Proceedings of the 18th European Symposium on Artificial Neural Networks (ESANN) 2010. Evere, Belgium: d-side publications.
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  • [80]
    2010 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 1994138
    Villmann, T., et al., 2010. The Mathematics of Divergence Based Online Learning in Vector Quanitzation. In N. El Gayar & F. Schwenker, eds. ANNPR'2010. Berlin, Heidelberg: Springer, pp. 108-119.
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  • [79]
    2010 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 1994227
    Villmann, T., Schleif, F.-M., & Hammer, B., 2010. Sparse representation of data. In M. Verleysen, ed. ESANN'10. D side, pp. 225-234.
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  • [78]
    2009 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 1993984
    Schleif, F.-M., et al., 2009. Cancer Informatics by Prototype-networks in Mass Spectrometry. Artificial Intelligence in Medicine, 45(2-3), p 215-228.
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  • [77]
    2009 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 1992551
    Schleif, F.-M., & Villmann, T., 2009. Neural Maps and Learning Vector Quantization - Theory and Applications. In Proceedings of the ESANN 2009. European Symposium on Artificial Neural Networks. Advances in Computational Intelligence and Learning. Evere, Belgium: d-side publications, pp. 509-516.
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  • [76]
    2009 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 1992570
    Simmuteit, S., et al., 2009. Hierarchical PCA using Tree-SOM for the Identification of Bacteria. In J. C. Príncipe & R. Miikkulainen, eds. Advances in Self-Organizing Maps. Proceedings of the 7th International Workshop on Self Organizing Maps WSOM 2009. LNCS, 5629. Berlin: Springer, pp. 272-280.
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  • [75]
    2009 | Sammelwerksbeitrag | Veröffentlicht | PUB-ID: 1992575
    Simmuteit, S., et al., 2009. Deconvolution and Identification of Mass Spectra from mixed and pure colonies of bacteria. In J. Blazewicz, K. Ecker, & B. Hammer, eds. ICOLE 2009. IfI-09-12. Clausthal-Zellerfeld, Germany: Technical University of Clausthal, pp. 104-112.
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  • [74]
    2009 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 1992607
    Villmann, T., & Schleif, F.-M., 2009. Functional Vector Quantization by Neural Maps. In Institute of Electrical and Electronics Engineers, ed. Proceedings of Whispers 2009. Piscataway, NJ: IEEE, pp. 636.
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  • [73]
    2009 | Sammelwerksbeitrag | Veröffentlicht | PUB-ID: 1994067
    Strickert, M., et al., 2009. Unleashing Pearson Correlation for Faithful Analysis of Biomedical Data. In M. Biehl, et al., eds. Similarity-based Clustering. LNAI, 5400. Berlin: Springer, pp. 70-91.
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  • [72]
    2009 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 1992547
    Schleif, F.-M., Biehl, M., & Vellido, A., 2009. Advances in machine learning and computational intelligence. NeuroComputing, 72(7-9), p 1377-1378.
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  • [71]
    2009 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 1992580
    Strickert, M., et al., 2009. Matrix metric adaptation for improved linear discriminant analysis of biomedical data. In J. Cabestany, et al., eds. Bio-Inspired Systems: Computational and Ambient Intelligence, 10th International Work-Conference on Artificial Neural Networks, IWANN 2009, Proceedings. LNCS, 5517. no.Part 1 Berlin: Springer, pp. 933-940.
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  • [70]
    2009 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 1992526
    Schleif, F.-M., Villmann, T., & Ongyerth, M., 2009. Supervised data analysis and reliability estimation for spectral data. NeuroComputing, 72(16-18), p 3590-3601.
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  • [69]
    2009 | Report | Veröffentlicht | PUB-ID: 1993316
    Biehl, M., et al., 2009. Stationarity of Matrix Relevance Learning Vector Quantization, Machine Learning Reports, Leipzig: Universität Leipzig.
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  • [68]
    2009 | Sammelwerksbeitrag | Veröffentlicht | PUB-ID: 1992534
    Schleif, F.-M., et al., 2009. Extended Targeted Profiling to Identify and Quantify Metabolites in 1-H NMR measurements. In J. Blazewicz, K. Ecker, & B. Hammer, eds. ICOLE 2009. IfI-09-12. Clausthal-Zellerfeld, Germany: Technical University of Clausthal, pp. 89-103.
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  • [67]
    2009 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 1992565
    Simmuteit, S., et al., 2009. Tanimoto metric in Tree-SOM for improved representation of mass spectrometry data with an underlying taxonomic structure. In Proceedings of ICMLA 2009. IEEE Press, pp. 563--567.
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  • [66]
    2009 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 1992517
    Schleif, F.-M., et al., 2009. Support Vector Classification of Proteomic Profile Spectra based on Feature Extraction with the Bi-orthogonal Discrete Wavelet Transform. Computing and Visualization in Science, 12(4), p 189-199.
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  • [65]
    2008 | Sammelwerksbeitrag | Veröffentlicht | PUB-ID: 1993939
    Schleif, F.-M., Villmann, T., & Hammer, B., 2008. Pattern Recognition by Supervised Relevance Neural Gas and its Application to Spectral Data in Bioinformatics. In J. R. -n R. -al Dopico, J. Dorado, & A. Pazos, eds. Encyclopedia of Artificial Intelligence. IGI Global, pp. 1337-1342.
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  • [64]
    2008 | Report | Veröffentlicht | PUB-ID: 1993379
    Bunte, K., et al., 2008. Discriminative Visualization by Limited Rank Matrix Learning, Machine Learning Reports, Leipzig: Universität Leipzig.
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  • [63]
    2008 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 1992530
    Schleif, F.-M., Ongyerth, M., & Villmann, T., 2008. Sparse coding Neural Gas for analysis of Nuclear Magnetic Resonance Spectroscopy. In Proceedings of the CBMS 2008. IEEE, pp. 620-625.
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  • [62]
    2008 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 1992554
    Schneider, P., et al., 2008. Generalized Matrix Learning Vector Quantizer for the Analysis of Spectral Data. In M. Verleysen, ed. Proceedings of the 16th European Symposium on Artificial Neural Networks (ESANN) 2008. Evere, Belgium: d-side publications, pp. 451-456.
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  • [61]
    2008 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 1992597
    Strickert, M., Schleif, F.-M., & Villmann, T., 2008. Metric adaptation for supervised attribute rating. In M. Verleysen, ed. Proceedings of the 16th European Symposium on Artificial Neural Networks (ESANN) 2008. Evere, Belgium: d-side publications, pp. 31-36.
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  • [60]
    2008 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 1993966
    Schleif, F.-M., Villmann, T., & Hammer, B., 2008. Prototype based Fuzzy Classification in Clinical Proteomics. International Journal of Approximate Reasoning, 47(1), p 4-16.
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  • [59]
    2008 | Sammelwerksbeitrag | Veröffentlicht | PUB-ID: 1993900
    Schleif, F.-M., Hammer, B., & Villmann, T., 2008. Analysis of Spectral Data in Clinical Proteomics by use of Learning Vector Quantizers. In M. Van de Werff, A. Delder, & R. Tollenaar, eds. Computational Intelligence in Biomedicine and Bioinformatics: Current Trends and Applications. Berlin: Springer, pp. 141-167.
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  • [58]
    2008 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 1992539
    Schleif, F.-M., et al., 2008. Automatic Identification and Quantification of Metabolites in H-NMR Measurements. In Proceedings of the Workshop on Computational Systems Biology (WCSB) 2008. pp. 165-168.
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  • [57]
    2008 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 1992589
    Strickert, M., Schleif, F.-M., & Seiffert, U., 2008. Derivatives of Pearson Correlation for Gradient-based Analysis of Biomedical Data. Ibero-American Journal of Artificial Intelligence, 37(12), p 37-44.
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  • [56]
    2008 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 1994253
    Villmann, T., et al., 2008. Classification of mass-spectrometric data in clinical proteomics using learning vector quantization methods. Briefings in Bioinformatics, 9(2), p 129-143.
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  • [55]
    2008 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 2017617
    Villmann, T., et al., 2008. Fuzzy Classification Using Information Theoretic Learning Vector Quantization. Neurocomputing, 71(16-18), p 3070-3076.
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  • [54]
    2008 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2001836
    Geweniger, T., et al., 2008. Comparison of cluster algorithms for the analysis of text data using Kolmogorov complexity. In M. Köppen, N. K. Kasabov, & G. G. Coghill, eds. ICONIP 2008. Berlin, Heidelberg: Springer, pp. 61-69.
    PUB | DOI
     
  • [53]
    2007 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 1994016 OA
    Schneider, P., et al., 2007. Advanced metric adaptation in Generalized LVQ for classification of mass spectrometry data. In Proceedings of 6th International Workshop on Self-Organizing Maps (WSOM 2007). Bielefeld: Bielefeld University.
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    2007 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 1994267 OA
    Villmann, T., et al., 2007. Class imaging of hyperspectral satellite remote sensing data using FLSOM. In Proceedings of 6th International Workshop on Self-Organizing Maps (WSOM 2007). Bielefeld: Bielefeld University.
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    2007 | Report | Veröffentlicht | PUB-ID: 1993922
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    Schleif, F.-M., Villmann, T., & Hammer, B., 2007. Analysis of Proteomic Spectral Data by Multi Resolution Analysis and Self-Organizing-Maps. In F. Masulli, S. Mitra, & G. Pasi, eds. Application of Fuzzy Sets Theory. Proceedings of the 7th International Workshop on Fuzzy Logic and Applications. LNAI 4578. Berlin, Heidelberg: Springer, pp. 563-570.
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    2007 | Sammelwerksbeitrag | Veröffentlicht | PUB-ID: 1992509
    Schleif, F.-M., 2007. Prototypen basiertes maschinelles Lernen in der klinischen Proteomik. In D. Wagner, ed. Ausgezeichnete Informatikdissertationen 2006. GI-Edition Lecture Notes in Informatics. Dissertation. no.7 Bonn: Gesellschaft für Informatik, pp. 179-188.
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    2007 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 1992507
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    2007 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 1992610
    Villmann, T., et al., 2007. Association learning in SOMs for Fuzzy-Classification. In 6th International Conference on Machine Learning and Applications, 2007. pp. 581-586.
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    2007 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 1993811
    Hasenfuss, A., et al., 2007. Neural gas clustering for dissimilarity data with continuous prototypes. In F. Sandoval, et al., eds. Computational and Ambient Intelligence – Proceedings of the 9th Work-conference on Artificial Neural Networks. LNCS 4507. Berlin: Springer, pp. 539-546.
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    2007 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 1992616
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    Deininger, S.-O., Gerhard, M., & Schleif, F.-M., 2007. Statistical Classification and Visualization of MALDI-Imaging Data. In Proc. of CBMS 2007. pp. 403-405.
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    Villmann, T., et al., 2007. Fuzzy Labeled Self Organizing Map for Clasification of Spectra. In F. Sandoval, et al., eds. Computational and Ambient Intelligence. Proceedings of the 9th Work-conference on Artificial Neural Networks. LNCS, 4507. Berlin: Springer, pp. 556-563.
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    Hasenfuss, A., et al., 2007. Neural gas clustering for sparse proximity data. In F. Sandoval, et al., eds. Proceedings of the 9th International Work-Conference on Artificial Neural Networks.LNCS 4507. Berlin, Heidelberg, Germany: Springer, pp. 539-546.
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    2007 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 1993907
    Schleif, F.-M., Hammer, B., & Villmann, T., 2007. Supervised Neural Gas for Functional Data and its Application to the Analysis of Clinical Proteom Spectra. In F. Sandoval, et al., eds. Computational and Ambient Intelligence. Proceedings of the 9th International Work-Conference on Artificial Neural Networks. LNCS, 4507. Berlin, Heidelberg: Springer, pp. 1036-1044.
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    2007 | Report | Veröffentlicht | PUB-ID: 1992505
    Schleif, F.-M., 2007. Preprocessing of Nuclear Magnetic Resonance Spectrometry Data, Machine Learning Reports, Leipzig: Universität Leipzig.
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    2007 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 1992586
    Strickert, M., & Schleif, F.-M., 2007. Supervised Attribute Relevance Determination for Protein Identification in Stress Experiments. In Proc. of MLSB 2007. pp. 81-86.
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    2007 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 1992593
    Strickert, M., Schleif, F.-M., & Seiffert, U., 2007. Gradients of Pearson Correlation for Analysis of Biomedical Data. In Proc. of ASAI 2007. pp. 139-150.
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    2006 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 1994184
    Villmann, T., et al., 2006. Prototype based classification using information theoretic learning. In I. King, et al., eds. Neural Information Processing, 13th International Conference. Proceedings. Lecture Notes in Computer Science, 4233. no.Part II Berlin: Springer, pp. 40-49.
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    2006 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 1994273
    Villmann, T., et al., 2006. Fuzzy Labeled Self-Organizing Map with Label-Adjusted Prototypes. In F. Schwenker, ed. Proceedings of Conference Artificial Neural Networks in Pattern Recognition. Berlin: Springer, pp. 46-56.
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    2006 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 1993578
    Hammer, B., et al., 2006. Supervised Batch Neural Gas. In F. Schwenker, ed. Proceedings of Conference Artificial Neural Networks in Pattern Recognition (ANNPR). Berlin: Springer Verlag, pp. 33-45.
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    Schleif, F.-M., Hammer, B., & Villmann, T., 2006. Margin based Active Learning for LVQ Networks. In M. Verleysen, ed. Proc. Of European Symposium on Artificial Neural Networks. Brussels, Belgium: d-side publications, pp. 539-544.
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    2006 | Dissertation | PUB-ID: 1992511
    Schleif, F.-M., 2006. Prototype based Machine Learning for Clinical Proteomics, Clausthal-Zellerfeld, Germany: Technical University Clausthal.
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    2006 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 1993889
    Schleif, F.-M., et al., 2006. Machine Learning and Soft-Computing in Bioinformatics. A Short Journey. In Proc. of FLINS 2006. World Scientific Press, pp. 541-548.
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    2006 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 1994237
    Villmann, T., Schleif, F.-M., & Hammer, B., 2006. Comparison of relevance learning vector quantization with other metric adaptive classification methods. Neural Networks, 19(5), p 610-622.
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    2006 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 1993568
    Hammer, B., et al., 2006. Supervised median neural gas. In C. Dagli, et al., eds. Smart Engineering System Design. Intelligent Engineering Systems Through Artificial Neural Networks, 16. ASME Press, pp. 623-633.
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    Hammer, B., et al., 2006. Supervised median clustering. In C. H. Dagli, 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. New York, NY: ASME Press, pp. 623-632.
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    2006 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 1993878
    Schleif, F.-M., et al., 2006. Analysis and Visualization of Proteomic Data by Fuzzy labeled Self-Organizing Maps. In D. J. Lee, et al., eds. 19th IEEE International Symposium on Computer- based Medical Systems. Los Alamitos: IEEE Computer Society Press, pp. 919-924.
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    2006 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 1992445
    Brüß, C., et al., 2006. Fuzzy Image Segmentation with Fuzzy Labelled Neural Gas. In Proc. of ESANN 2006. pp. 563-569.
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    2006 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 1994195
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    2006 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 1994241
    Villmann, T., Schleif, F.-M., & Hammer, B., 2006. Prototype-based fuzzy classification with local relevance for proteomics. Neurocomputing, 69(16-18), p 2425-2428.
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    2006 | Report | Veröffentlicht | PUB-ID: 1993584
    Hammer, B., et al., 2006. Supervised median clustering, IfI Technical reports, Clausthal-Zellerfeld: Clausthal University of Technology.
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    2006 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2017225
    Hammer, B., et al., 2006. Learning vector quantization classification with local relevance determination for medical data. In L. Rutkowski, et al., eds. Artificial Intelligence and Soft-Computing - Proceedings of ICAISC 2006. LNAI, 4029. Lecture notes in computer science ; 4029 : Lecture notes in artificial intelligence. no.4029 Berlin, Heidelberg: Springer, pp. 603-612.
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    2005 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 1994172
    Villmann, T., et al., 2005. Fuzzy Labeled Neural GAS for Fuzzy Classification. In M. Cottrell, ed. Proceedings of the 5th Workshop on Self-Organizing Maps [on CD-ROM]. Paris, France: University Paris-1-Pantheon-Sorbonne, pp. 283-290.
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    2005 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 1992513
    Schleif, F.-M., 2005. Plugins mit wxWidgets. Offene Systeme, 2005(1), p 5-10.
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    2005 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 1994219
    Villmann, T., Schleif, F.-M., & Hammer, B., 2005. Fuzzy Classification for Classification of Mass Spectrometric Data Based on Learning Vector Quantization. In International Workshop on Integrative Bioinformatics.
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    2005 | Report | Veröffentlicht | PUB-ID: 1993675
    Hammer, B., Schleif, F.-M., & Villmann, T., 2005. On the Generalization Ability of Prototype-Based Classifiers with Local Relevance Determination, IfI Technical reports, Clausthal-Zellerfeld: Clausthal University of Technology.
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    2005 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 1994249
    Villmann, T., Schleif, F.-M., & Hammer, B., 2005. Fuzzy labeled soft nearest neighbor classification with relevance learning. In M. A. Wani, K. J. Cios, & K. Hafeez, eds. Proceedings of the International Conference of Machine Learning Applications. Los Angeles: IEEE Press, pp. 11-15.
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    2005 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 1993974
    Schleif, F.-M., Villmann, T., & Hammer, B., 2005. Local Metric Adaptation for Soft Nearest Prototype Classification to Classify Proteomic Data. In I. Bloch, A. Petrosino, & A. G. B. Tettamanzi, eds. Proceedings of the 6th Workshop on Fuzzy Logic and Applications. Berlin, Heidelberg: Springer, pp. 290-296.
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    2004 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 1994168
    Villmann, T., Hammer, B., & Schleif, F.-M., 2004. Metrik Adaptation for Optimal Feature Classification in Learning Vector Quantization Applied to Environment Detection. In H. - M. Groß, K. Debes, & H. - J. Böhme, eds. Proceedings of Selbstorganisation Von Adaptivem Verfahren. Fortschritts-Berichte VDI Reihe 10, Nr. 742. VDI Verlag, pp. 592-597.
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    2004 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 1994212
    Villmann, T., Schleif, F.-M., & Hammer, B., 2004. 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. VDI Verlag.
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    2004 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 1993870
    Schleif, F.-M., et al., 2004. Supervised Relevance Neural Gas and Unified Maximum Separability Analysis for Classification of Mass Spectrometric Data. In M. A. Wani, K. J. Cios, & K. Hafeez, eds. Proceedings of the 3rd International Conference on Machine Learning and Applications (ICMLA) 2004. Los Alamitos, CA, USA: IEEE Press, pp. 374-379.
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    2003 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 1994223
    Villmann, T., Schleif, F.-M., & Hammer, B., 2003. Supervised Neural Gas and Relevance Learning in Learning Vector Quantization. In T. Yamakawa, ed. Proceedings of the 4th Workshop on Self Organizing Maps [on CD-ROM]. Hibikino, Kitakyushu, Japan: Kyushu Institute of Technology, pp. 47-52.
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    2003 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 1992477
    Köhler, M., et al., 2003. A mission for the EEG coherence analysis: Is the task complex or difficult? Brain Topography, 15(4), p 271.
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    2003 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 1992456
    Dörfler, T., et al., 2003. Working memory load and EEG coherence. Brain Topography, 15(4), p 269.
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    2003 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 1992466
    Gruhn, V., et al., 2003. A distributed logistic support communication system. In H. Linger, et al., eds. Proceedings of ISD 2003 - Constructing the Infrastructure for the Knowledge Economy - Methods and Tools, Theory and Practice. London: Kluwer Academic Publishers, pp. 705-713.
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    2002 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 1992544
    Schleif, F.-M., & Stamer, H., 2002. {LaTeX} im studentischen Alltag. Gaotenblatt, , p 3-10.
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    2002 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 1992483
    Köhler, M., et al., 2002. Complexity and difficulty in memory based comparison. In J. A. da Silva, N. P. R. Filho, & E. H. Matsushima, eds. Proceedings of the 18th Meeting of the International Society for Psychophysics. Pabst Publishing, pp. 433-439.
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    2002 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 1992515
    Schleif, F.-M., 2002. OCR mit statistischen Momenten. Gaotenblatt, 2002, p 15-17.
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    2001 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 1992560
    Simmel, A., et al., 2001. An analysis of connections between internal and external learning process indicators using EEG coherence analysis. In Proceedings of the 17th Meeting of the International Society for Psychophysics. Pabst Publishing, pp. 602-607.
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    2001 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 1992461
    Dörfler, T., et al., 2001. Complexity - dependent synchronization of brain subsystems during memorization. In Proceedings of the 17th Meeting of the International Society for Psychophysics. Pabst Publishing, pp. 343-348.
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