- My Google Scholar page
- For recent publications look at news or DBLP
- If you are interested on a specific publication I am a (co-)author of or one of the developed algorithms, not listed on this page, send an email to frank-michael.schleif [AT] fhws [- d - o - t] de
- Material for the paper Indefinite proximity learning - A review
- Selected papers are listed below:
[2] Andrej Gisbrecht, Frank-Michael Schleif: Metric and non-metric proximity transformations at linear costs. Neurocomputing 167: 643-657 (2015)
[3] Frank-Michael Schleif, Peter Tiño: Indefinite Proximity Learning: A Review. Neural Computation 27(10): 2039-2096 (2015)
[4] Barbara Hammer, Daniela Hofmann, Frank-Michael Schleif, Xibin Zhu: Learning vector quantization for (dis-)similarities. Neurocomputing 131: 43-51 (2014)
[5] Marc Strickert, Kerstin Bunte, Frank-Michael Schleif, Eyke Hüllermeier: Correlation-based embedding of pairwise score data. Neurocomputing 141: 97-109 (2014)
[6] Xibin Zhu, Frank-Michael Schleif, Barbara Hammer: Adaptive conformal semi-supervised vector quantization for dissimilarity data. Pattern Recognition Letters 49: 138-145 (2014)
[7] Andrej Gisbrecht, Bassam Mokbel, Frank-Michael Schleif, Xibin Zhu, Barbara Hammer: Linear Time Relational Prototype Based Learning. Int. J. Neural Syst. 22(5) (2012)
[8] Kerstin Bunte, Petra Schneider, Barbara Hammer, Frank-Michael Schleif, Thomas Villmann, Michael Biehl: Limited Rank Matrix Learning, discriminative dimension reduction and visualization. Neural Networks 26: 159-173 (2012)
[9] Frank-Michael Schleif, Thomas Villmann, Barbara Hammer, Petra Schneider: Efficient Kernelized Prototype Based Classification. Int. J. Neural Syst. 21(6): 443-457 (2011)
[10] Frank-Michael Schleif, Thomas Villmann, Markus Kostrzewa, Barbara Hammer, Alexander Gammerman: Cancer informatics by prototype networks in mass spectrometry. Artificial Intelligence in Medicine 45(2-3): 215-228 (2009)