
The series contains reviewed reports on Machine Learning (Machine Learning Reports) [MLR] (citation see bottom) 
This webpage is the platform to access these reports. In case of any questions contact the Editors (see above). 

2007 

Report 01:Preprocessing of Nuclear Magnetic Resonance Spectrometry Data 
Report 02:Aggregation of multiple peaklists by use of an improved Neural Gas Network 
Report 03:Sobolev Metrics for Learning of Functional Data  Mathematical and Theoretical Aspects 

2008 

Report 01:Combining Phenotypic and Genotypic Learning 
Report 02:Regularization in Matrix Relevance Learning 
Report 03:Discriminative Visualization by Limited Rank Matrix Learning 

2009 

Report 01:Stationarity of Matrix Relevance Learning Vector Quantization 
Report 02:Extending RSLVQ to handle data points with uncertain class assignments 
Report 03:Mathematical Aspects of Divergence Based Vector Quantization Using FrechetDerivatives 

2010 

Report 01:Mathematical Aspects of Divergence Based Vector Quantization Using FrechetDerivatives  Extended and revised version 
Report 02:Mathematical Foundations of the Generalization of tSNE and SNE for Arbitrary Divergences 
Report 03:Mathematical Foundations of the Self Organized Neighbor Embedding (SONE) for Dimension Reduction and Visualization 
Report 04:Proceedings of the Workshop  New Challenges in Neural Computation 2010 
Report 05:Proceedings of the Mittweida Workshop on Computational Intelligence  MIWOCI 2010 

2011 

Report 01:Proceedings of the GermanPolish Workshop on Computational Biology, Scheduling and Machine Learning (ICOLE'2010) 
Report 02:Fuzzy Supervised Neural Gas for Semisupervised Vector Quantization  Theoretical Aspects 
Report 03:About Sparsity in Functional Relevance Learning in Generalized Learning Vector Quantization 
Report 04:Classification of Hyperspectral Imagery with Neural Networks: Comparison to Conventional Tools 
Report 05:Proceedings of the Workshop  New Challenges in Neural Computation 2011 
Report 06:Proceedings of the Mittweida Workshop on Computational Intelligence  MIWOCI 2011 
Report 07:Derivation of a Generalized ConnIndex for Fuzzy Clustering Validation 

2012 

Report 01:Proceedings of the GermanPolish Workshop on Computational Biology, Scheduling and Machine Learning (ICOLE'2011) 
Report 02:A Note on Gradient Based Learning in Vector Quantization Using Differentiable Kernels for Hilbert and Banach Spaces 
Report 03:Proceedings of the Workshop  New Challenges in Neural Computation 2012 
Report 04:Data analysis of (non)metric (dis)similarities at linear costs (This approach has been published in a Simbad 2013 paper) 
Report 05:SOMbased topology visualization for interactive analysis of highdimensional large datasets 
Report 06:Proceedings of the Mittweida Workshop on Computational Intelligence  MIWOCI 2012 

2013 
Report 01:Detection of Targets in Characteristic GPR Sensor Data Using Machine Learning Techniques 
Report 02:Workshop New Challenges in Neural Computation 2013 
Report 03:Large scale Nyström approximation for nonmetric similarity and dissimilarity data 
Report 04:Proceedings of the Mittweida Workshop on Computational Intelligence  MIWOCI 2013 
Report 05:Analysis of temporal Kinect motion capturing data 
Report 06:About Learning of Supervised Generative Models for Dissimilarity Data 

2014 
Report 01:Proceedings of the Mittweida Workshop on Computational Intelligence  MIWOCI 2014 
Report 02:Workshop New Challenges in Neural Computation 2014 
Report 03:Median Variants of LVQ for Optimization of Statistical Quality Measures for Classification of Dissimilarity Data 

2015 
Report 01:An Application of the Generalized Matrix Learning Vector Quantization Method for Cutoffline Classification of AutomobileHeadlights 
Report 02:A Comment on the Functional L_p^{TS} Measure Regarding the Norm Properties 
Report 03:Workshop New Challenges in Neural Computation 2015 
Report 04:Proceedings of ICOLE Workshop  2015 

2016 
Report 01:A Probabilistic Classifier Model with Adaptive Rejection Option 
Report 02:not yet available 
Report 03:Proceedings of the Mittweida Workshop on Computational Intelligence  MIWOCI 2016 
Report 04:Workshop New Challenges in Neural Computation 2016 

Citation please as: e.g.
@PROCEEDINGS{MLR0107,
editor = "Thomas Villmann and FrankMichael Schleif",
title = "Machine Learning Reports 01/2007",
series = "Machine Learning Reports",
year = {2007},
volume = {1},
number = {MLR012007},
note = "ISSN:18653960 http://www.techfak.unibielefeld.de/$\tilde{ }$fschleif/mlr/mlr\_01\_2007.pdf",
}
@inproceedings{MLR0107/Schleif2007a,
author = {F.M. Schleif},
title = {Preprocessing of Nuclear Magnetic Resonance Spectrometry Data},
booktitle = {Machine Learning Reports 01/2007}
year = {2007},
pages = {XXYY},
note = "ISSN:18653960 http://www.techfak.unibielefeld.de/$\tilde{ }$fschleif/mlr/mlr\_01\_2007.pdf",
crossref = {MLR0107}
}

stylesheet: style files (LaTeX) 