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. 2015;74(1-2):95-116.
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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. 2015;169(SI):306-322.
    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, Loog M, eds. Similarity-Based Pattern Recognition. Similarity-Based Pattern Recognition : Third International Workshop, SIMBAD 2015, Proceedings. Lecture Notes in Computer Science. Vol 9370. Cham: Springer International Publishing; 2015: 160-170.
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  • [130]
    2015 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2910619
    Schleif F-M, Villmann T, Zhu X. High Dimensional Matrix Relevance Learning. In: 2014 IEEE International Conference on Data Mining Workshop. Piscataway, NJ: IEEE; 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. 2015;167:643-657.
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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. 2014;141:84-96.
    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. 2014;49:138-145.
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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. 2014;131:43-51.
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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. 2014;141:97-109.
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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, et al., eds. Intelligent Data Engineering and Automated Learning – IDEAL 2013. Lecture Notes in Computer Science. Berlin, Heidelberg: Springer Berlin Heidelberg; 2013: 302-309.
    PUB | DOI
     
  • [123]
    2013 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 2612731
    Micheli A, Schleif F-M, Tino P. Novel approaches in machine learning and computational intelligence. Neurocomputing. 2013;112:1-3.
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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, Cabestany J, eds. IWANN (1). Lecture Notes in Computer Science. Vol 7902. Springer; 2013: 347-356.
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  • [120]
    2013 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2615724
    Schleif F-M, Gisbrecht A. Data Analysis of (Non-)Metric Proximities at Linear Costs. In: Proceedings of SIMBAD 2013. Berlin, Heidelberg: Springer; 2013: 59-74.
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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. In: Proceedings of ESANN 2013. 2013: 89-94.
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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. 2012;22(05): 1250021.
    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. In: Proceedings of ESANN 2012. 2012: 387-392.
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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, Institute of Electrical and Electronics Engineers, eds. IJCNN. Piscataway, NJ: IEEE; 2012: 1-8.
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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. In: Proceedings of ICPR 2012. IEEE; 2012: 1229-1232.
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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. In: Proceedings of MCSD 2012. Berlin, Heidelberg: Springer Berlin Heidelberg; 2012: 141-151.
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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. In: ICANN (2). Lecture Notes in Computer Science. Vol 7553. Berlin, Heidelberg: Springer Berlin Heidelberg; 2012: 531-539.
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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. 2012;26:159-173.
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  • [111]
    2012 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2534888
    Schleif F-M, Zhu X, Hammer B. A Conformal Classifier for Dissimilarity Data. In: AIAI (2). Berlin, Heidelberg: Springer Berlin Heidelberg; 2012: 234-243.
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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. In: ISNN (1). Berlin, Heidelberg: Springer Berlin Heidelberg; 2012: 55-63.
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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. In: HAIS (1). Berlin, Heidelberg: Springer Berlin Heidelberg; 2012: 309-321.
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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, Institute of Electrical and Electronics Engineers, eds. IJCNN. Piscataway, NJ: IEEE; 2012: 1-8.
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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. 2012;90:72-84.
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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, Honkela T, eds. Advances in Self-Organizing Maps. Lecture Notes in Computer Science. Berlin, Heidelberg: Springer Berlin Heidelberg; 2011: 1-15.
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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, Kwok J, eds. Neural Information Processing. Lecture Notes in Computer Science. Berlin, Heidelberg: Springer Berlin Heidelberg; 2011: 481-489.
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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, Hollmén J, eds. Advances in Intelligent Data Analysis X. Lecture Notes in Computer Science. Berlin, Heidelberg: Springer Berlin Heidelberg; 2011: 185-197.
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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, Kaski S, eds. Artificial Neural Networks and Machine Learning – ICANN 2011. Lecture Notes in Computer Science. Berlin, Heidelberg: Springer Berlin Heidelberg; 2011: 150-158.
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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. 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. Vol 6936. Berlin, Heidelberg: Springer; 2011: 25-33.
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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. In: 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, 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. In: Proceedings of ESANN 2011. 2011: 77-86.
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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. In: Proceedings of ESANN 2011. Ciaco - i6doc.com; 2011: 29-34.
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  • [96]
    2011 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 2290045
    Lee JA, Schleif F-M, Martinetz T. Advances in artificial neural networks, machine learning, and computational intelligence. Neurocomputing. 2011;74(9):1299-1300.
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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. 2011;21(06):443-457.
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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. In: IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology. 2011: pp.154-161.
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  • [93]
    2011 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 2276654
    Schleif F-M. Sparse Kernel Vector Quantization with Local Dependencies. In: Proceedings of IJCNN 2011. 2011: accepted.
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  • [92]
    2011 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 1992489
    Mwebaze E, Schneider P, Schleif F-M, et al. Divergence based classification in Learning Vector Quantization. Neurocomputing. 2011;74(9):1429-1435.
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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. 2011;27(4):524-533.
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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. In: Proceedings of AIA 2011. 2011: in press.
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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. In: Proceedings of ESANN 2011. 2011: 17-22.
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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. In: Proceedings of WCSB 2010. 2010: 103-106.
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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, Zurada J, eds. Artificial Intelligence and Soft Computing. Lecture Notes in Computer Science, 6113. Berlin, Heidelberg: Springer; 2010: 479-486.
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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. In: 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. In: Proceedings of WCSB 2010. 2010: 91-94.
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  • [84]
    2010 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 1992441
    Angulo C, Lee JA, Schleif F-M. Advances in computational intelligence and learning. NeuroComputing. 2010;73(7-9):1049-1050.
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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, Yin H, eds. Intelligent Data Engineering and Automated Learning – IDEAL 2010 11th International Conference, Paisley, UK, September 1-3, 2010. Proceedings. Berlin u.a.: Springer; 2010: 21-28.
    PUB | DOI
     
  • [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. 2010;25(2):327-343.
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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. In: Proceedings of the 18th European Symposium on Artificial Neural Networks (ESANN) 2010. Evere, Belgium: d-side publications; 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, Schwenker F, eds. ANNPR'2010. Berlin, Heidelberg: Springer; 2010: 108-119.
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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. D side; 2010: 225-234.
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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. 2009;45(2-3):215-228.
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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. In: Proceedings of the ESANN 2009. European Symposium on Artificial Neural Networks. Advances in Computational Intelligence and Learning. Evere, Belgium: d-side publications; 2009: 509-516.
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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 JC, Miikkulainen R, eds. Advances in Self-Organizing Maps. Proceedings of the 7th International Workshop on Self Organizing Maps WSOM 2009. LNCS, 5629. Berlin: Springer; 2009: 272-280.
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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, Hammer B, eds. ICOLE 2009. IfI-09-12. Clausthal-Zellerfeld, Germany: Technical University of Clausthal; 2009: 104-112.
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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. Piscataway, NJ: IEEE; 2009: 636.
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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, Villmann T, eds. Similarity-based Clustering. LNAI, 5400. Berlin: Springer; 2009: 70-91.
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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. 2009;72(7-9):1377-1378.
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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, Corchado JM, eds. Bio-Inspired Systems: Computational and Ambient Intelligence, 10th International Work-Conference on Artificial Neural Networks, IWANN 2009, Proceedings. LNCS, 5517. Vol Part 1. Berlin: Springer; 2009: 933-940.
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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. 2009;72(16-18):3590-3601.
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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. Leipzig: Universität 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, Hammer B, eds. ICOLE 2009. IfI-09-12. Clausthal-Zellerfeld, Germany: Technical University of Clausthal; 2009: 89-103.
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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. In: Proceedings of ICMLA 2009. IEEE Press; 2009: 563--567.
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  • [66]
    2009 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 1992517
    Schleif F-M, Lindemann M, Maass P, et al. Support Vector Classification of Proteomic Profile Spectra based on Feature Extraction with the Bi-orthogonal Discrete Wavelet Transform. Computing and Visualization in Science. 2009;12(4):189-199.
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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 JR-n R-al, Dorado J, Pazos A, eds. Encyclopedia of Artificial Intelligence. IGI Global; 2008: 1337-1342.
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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. Leipzig: Universität 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. In: Proceedings of the CBMS 2008. IEEE; 2008: 620-625.
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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. Evere, Belgium: d-side publications; 2008: 451-456.
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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. Evere, Belgium: d-side publications; 2008: 31-36.
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  • [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. 2008;47(1):4-16.
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  • [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, Tollenaar R, eds. Computational Intelligence in Biomedicine and Bioinformatics: Current Trends and Applications. Berlin: Springer; 2008: 141-167.
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  • [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. In: Proceedings of the Workshop on Computational Systems Biology (WCSB) 2008. 2008: 165-168.
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  • [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. 2008;37(12):37-44.
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  • [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. 2008;9(2):129-143.
    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. 2008;71(16-18):3070-3076.
    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 NK, Coghill GG, eds. ICONIP 2008. Berlin, Heidelberg: Springer; 2008: 61-69.
    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. In: Proceedings of 6th International Workshop on Self-Organizing Maps (WSOM 2007). Bielefeld: Bielefeld University; 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. In: Proceedings of 6th International Workshop on Self-Organizing Maps (WSOM 2007). Bielefeld: Bielefeld University; 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. In: Proceedings of International Joint Conference on Neural Networks. IEEE; 2007: 1877-1882.
    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, Villmann T, eds. Similarity-based Clustering and its Application to Medicine and Biology. Dagstuhl Seminar Proceedings. Dagstuhl, Germany: Internationales Begegnungs- und Forschungszentrum für Informatik (IBFI), Schloss 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. Leipzig: Universität 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. In: Similarity based Clustering. Lecture Notes in Artificial Intelligence, 5400. Vol 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, Pasi G, eds. Application of Fuzzy Sets Theory. Proceedings of the 7th International Workshop on Fuzzy Logic and Applications. LNAI 4578. Berlin, Heidelberg: Springer; 2007: 563-570.
    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. Vol 7. Bonn: Gesellschaft für Informatik; 2007: 179-188.
    PUB
     
  • [45]
    2007 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 1993911
    Schleif F-M, Hammer B, Villmann T. Margin based Active Learning for LVQ Networks. Neurocomputing. 2007;70(7-9):1215-1224.
    PUB | DOI | WoS
     
  • [44]
    2007 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 1992507
    Schleif F-M. Maschinelles Lernen mit Prototypmethoden in der klinischen Proteomik. KI - Künstliche Intelligenz. 2007;(4):65-67.
    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. In: 6th International Conference on Machine Learning and Applications, 2007. 2007: 581-586.
    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, Grana M, eds. Computational and Ambient Intelligence – Proceedings of the 9th Work-conference on Artificial Neural Networks. LNCS 4507. Berlin: Springer; 2007: 539-546.
    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. Evere, Belgium: d-side publications; 2007: 103-108.
    PUB
     
  • [40]
    2007 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 1992452
    Deininger S-O, Gerhard M, Schleif F-M. Statistical Classification and Visualization of MALDI-Imaging Data. In: Proc. of CBMS 2007. 2007: 403-405.
    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, Grana M, eds. Computational and Ambient Intelligence. Proceedings of the 9th Work-conference on Artificial Neural Networks. LNCS, 4507. Berlin: Springer; 2007: 556-563.
    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, Grana M, eds. Proceedings of the 9th International Work-Conference on Artificial Neural Networks.LNCS 4507. Berlin, Heidelberg, Germany: Springer; 2007: 539-546.
    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, Grana M, eds. Computational and Ambient Intelligence. Proceedings of the 9th International Work-Conference on Artificial Neural Networks. LNCS, 4507. Berlin, Heidelberg: Springer; 2007: 1036-1044.
    PUB | DOI
     
  • [36]
    2007 | Report | Veröffentlicht | PUB-ID: 1992505
    Schleif F-M. Preprocessing of Nuclear Magnetic Resonance Spectrometry Data. Machine Learning Reports. Leipzig: Universität 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. In: Proc. of MLSB 2007. 2007: 81-86.
    PUB
     
  • [34]
    2007 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 1992593
    Strickert M, Schleif F-M, Seiffert U. Gradients of Pearson Correlation for Analysis of Biomedical Data. In: Proc. of ASAI 2007. 2007: 139-150.
    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, Wang DLL, eds. Neural Information Processing, 13th International Conference. Proceedings. Lecture Notes in Computer Science, 4233. Vol Part II. Berlin: Springer; 2006: 40-49.
    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. Berlin: Springer; 2006: 46-56.
    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). Berlin: Springer Verlag; 2006: 33-45.
    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. Brussels, Belgium: d-side publications; 2006: 539-544.
    PUB
     
  • [29]
    2006 | Dissertation | PUB-ID: 1992511
    Schleif F-M. Prototype based Machine Learning for Clinical Proteomics. Clausthal-Zellerfeld, Germany: Technical University Clausthal; 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. In: Proc. of FLINS 2006. World Scientific Press; 2006: 541-548.
    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. 2006;19(5):610-622.
    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, Ersoy O, eds. Smart Engineering System Design. Intelligent Engineering Systems Through Artificial Neural Networks, 16. ASME Press; 2006: 623-633.
    PUB
     
  • [25]
    2006 | Konferenzbeitrag | Veröffentlicht | PUB-ID: 1993594
    Hammer B, Hasenfuss A, Schleif F-M, Villmann T. Supervised median clustering. In: Dagli CH, 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; 2006: 623-632.
    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 DJ, Nutter B, Antani S, Mitra S, Archibald J, eds. 19th IEEE International Symposium on Computer- based Medical Systems. Los Alamitos: IEEE Computer Society Press; 2006: 919-924.
    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. In: Proc. of ESANN 2006. 2006: 563-569.
    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. 2006;19(6-7):772-779.
    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. 2006;69(16-18):2425-2428.
    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-Zellerfeld: Clausthal University of Technology; 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 LA, 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. Vol 4029. Berlin, Heidelberg: Springer; 2006: 603-612.
    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]. Paris, France: University Paris-1-Pantheon-Sorbonne; 2005: 283-290.
    PUB
     
  • [17]
    2005 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 1992513
    Schleif F-M. Plugins mit wxWidgets. Offene Systeme. 2005;2005(1):5-10.
    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. In: 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-Zellerfeld: Clausthal University of Technology; 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 MA, Cios KJ, Hafeez K, eds. Proceedings of the International Conference of Machine Learning Applications. Los Angeles: IEEE Press; 2005: 11-15.
    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, Tettamanzi AGB, eds. Proceedings of the 6th Workshop on Fuzzy Logic and Applications. Berlin, Heidelberg: Springer; 2005: 290-296.
    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, Böhme H-J, eds. Proceedings of Selbstorganisation Von Adaptivem Verfahren. Fortschritts-Berichte VDI Reihe 10, Nr. 742. VDI Verlag; 2004: 592-597.
    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, 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 MA, Cios KJ, Hafeez K, eds. Proceedings of the 3rd International Conference on Machine Learning and Applications (ICMLA) 2004. Los Alamitos, CA, USA: IEEE Press; 2004: 374-379.
    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]. Hibikino, Kitakyushu, Japan: Kyushu Institute of Technology; 2003: 47-52.
    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. 2003;15(4):271.
    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. 2003;15(4):269.
    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 WG, Zupancic J, Vigo K, Arnold J, eds. Proceedings of ISD 2003 - Constructing the Infrastructure for the Knowledge Economy - Methods and Tools, Theory and Practice. London: Kluwer Academic Publishers; 2003: 705-713.
    PUB
     
  • [5]
    2002 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 1992544
    Schleif F-M, Stamer H. {LaTeX} im studentischen Alltag. Gaotenblatt. 2002:3-10.
    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 JA, Filho NPR, Matsushima EH, eds. Proceedings of the 18th Meeting of the International Society for Psychophysics. Pabst Publishing; 2002: 433-439.
    PUB
     
  • [3]
    2002 | Zeitschriftenaufsatz | Veröffentlicht | PUB-ID: 1992515
    Schleif F-M. OCR mit statistischen Momenten. Gaotenblatt. 2002;2002:15-17.
    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. In: Proceedings of the 17th Meeting of the International Society for Psychophysics. Pabst Publishing; 2001: 602-607.
    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. In: Proceedings of the 17th Meeting of the International Society for Psychophysics. Pabst Publishing; 2001: 343-348.
    PUB
     
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