Machine Learning reports


Series of recent results in Machine Learning Research

Editors Prof. Dr. rer. nat. habil. Thomas Villmann (1) and Dr. rer. nat. Frank-Michael Schleif (2)

University of Applied Sciences Mittweida
Dept. of Mathematics/Physics/Computer Sciences
Technikumplatz 17
D-09648 Mittweida

E-Mail: villmann at hsmw nospam dot de
Telefon: +49 (0) 3727 58 1328
Fax: +49 (0) 3727 58 1315


University of Bielefeld
CITEC - AG Theoretical Computer Science
D-33594 Bielefeld

E-Mail: schleif at informatik nospam dot uni-leipzig dot de
Telefon: +49 (0) 521 106 12137
Fax: +49 (0) 521 106 6560 (please add a note on each page)

Submissions can be made to the following address

fschleif [ at ] techfak [dot] uni-bielefeld [dot] de

Publication rights and copyright of the articles remain with the authors


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 Frechet-Derivatives

2010


Report 01:Mathematical Aspects of Divergence Based Vector Quantization Using Frechet-Derivatives - Extended and revised version
Report 02:Mathematical Foundations of the Generalization of t-SNE 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 German-Polish Workshop on Computational Biology, Scheduling and Machine Learning (ICOLE'2010)
Report 02:Fuzzy Supervised Neural Gas for Semi-supervised 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 Conn-Index for Fuzzy Clustering Validation

2012


Report 01:Proceedings of the German-Polish 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:SOM-based topology visualization for interactive analysis of high-dimensional 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 non-metric 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 Cut-off-line Classification of Automobile-Headlights
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 Frank-Michael Schleif",
title = "Machine Learning Reports 01/2007",
series = "Machine Learning Reports",
year = {2007},
volume = {1},
number = {MLR-01-2007},
note = "ISSN:1865-3960 http://www.techfak.uni-bielefeld.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 = {XX-YY},
note = "ISSN:1865-3960 http://www.techfak.uni-bielefeld.de/$\tilde{ }$fschleif/mlr/mlr\_01\_2007.pdf",
crossref = {MLR0107}
}

stylesheet: style files (LaTeX)