Open AccessProceedings Article
Divergence Based Learning Vector Quantization
Ernest Mwebaze,Petra Schneider,Frank-Michael Schleif,Sven Haase,Thomas Villmann,Michael Biehl +5 more
- 01 Jan 2010
- pp 247-252
TL;DR: The use of alternative distance measures for similarity based classification in Learning Vector Quantization based on the so-called CauchySchwarz divergence and a non-symmetric Renyi divergence is suggested.
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Abstract: We suggest the use of alternative distance measures for similarity based classification in Learning Vector Quantization. Divergences can be employed whenever the data consists of non-negative normalized features, which is the case for, e.g., spectral data or histograms. As examples, we derive gradient based training algorithms in the framework of Generalized Learning Vector Quantization based on the so-called CauchySchwarz divergence and a non-symmetric Renyi divergence. As a first test we apply the methods to two different biomedical data sets and compare with the use of standard Euclidean distance.
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Citations
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References
An introduction to ROC analysis
TL;DR: The purpose of this article is to serve as an introduction to ROC graphs and as a guide for using them in research.
21.3K
•Book
Self-Organizing Maps
Teuvo Kohonen
- 01 Jan 1995
TL;DR: The Self-Organising Map (SOM) algorithm was introduced by the author in 1981 as mentioned in this paper, and many applications form one of the major approaches to the contemporary artificial neural networks field, and new technologies have already been based on it.
13.1K
Clustering with Bregman Divergences
Arindam Banerjee,Srujana Merugu,Inderjit S. Dhillon,Joydeep Ghosh +3 more
- 01 Dec 2005
TL;DR: This paper proposes and analyzes parametric hard and soft clustering algorithms based on a large class of distortion functions known as Bregman divergences, and shows that there is a bijection between regular exponential families and a largeclass of BRegman diverGences, that is called regular Breg man divergence.
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