Thursday, February 24, 2011 - 10:00 - 12:00
Prof. Dr. M. Biehl, Groningen
Similarity based classification techniques, such as Kohonen's Learning Vector Quantization (LVQ), can be improved significantly by introducing adaptive distance measures. In this talk, the recently developed Matrix Relevance LVQ (MRLVQ) is introduced and discussed in terms of an example application from the medical domain. Here, the aim is the classification of adrenal tumors based on urinary steroid excretion. MRLVQ provides a criterion for the selection of a reduced set of most discriminative bio-markers and facilitates the development of a non-invasive, highly sensitive diagnosis tool.