A Median Variant of Generalized Learning Vector Quantization
Nebel, David
Nebel
David
Hammer, Barbara
Hammer
Barbara
Villmann, Thomas
Villmann
Thomas
We introduce a median variant of the Generalized Learning Vector Quantization (GLVQ) algorithm. Thus, GLVQ can be used for classification problem learning, for which only dissimilarity information between the objects to be classified is available. For this purpose, the cost function of GLVQ is reformulated as a probabilistic model such that a generalized expectation maximization scheme can be applied as learning procedure. We give a rigorous mathematical proof for the new approach. Exemplary examples demonstrate the performance and the behavior of the algorithm.
19-26
19-26
Springer Berlin Heidelberg
2013