Adaptive tangent distances in generalized learning vector quantization for transformation and distortion invariant classification learning

2016 ◽  
Author(s):  
Sascha Saralajew ◽  
Thomas Villmann
Keyword(s):  
Vector Quantization ◽  
Learning Vector Quantization ◽  
Classification Learning

scholarly journals Aspects in Classification Learning - Review of Recent Developments in Learning Vector Quantization

2014 ◽  
Vol 39 (2) ◽  
pp. 79-105 ◽  
Author(s):  
M. Kaden ◽  
M. Lange ◽  
D. Nebel ◽  
M. Riedel ◽  
T. Geweniger ◽  
...  
Keyword(s):  
Machine Learning ◽  
Vector Quantization ◽  
Incomplete Data ◽  
Learning Vector Quantization ◽  
Classification Models ◽  
Classification Learning ◽  
Recent Developments ◽  
Optimal Classification ◽  
Classification Tasks ◽  
Basic Learning

Abstract .Classification is one of the most frequent tasks in machine learning. However, the variety of classification tasks as well as classifier methods is huge. Thus the question is coming up: which classifier is suitable for a given problem or how can we utilize a certain classifier model for different tasks in classification learning. This paper focuses on learning vector quantization classifiers as one of the most intuitive prototype based classification models. Recent extensions and modifications of the basic learning vector quantization algorithm, which are proposed in the last years, are highlighted and also discussed in relation to particular classification task scenarios like imbalanced and/or incomplete data, prior data knowledge, classification guarantees or adaptive data metrics for optimal classification.

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