Similarity-based learning methods have a great potential as an intuitive and ?exible toolbox
for mining visualization and inspection of largedata sets. They combine simple and
human-understandable principles such as distance-based classi?cation prototypes or Hebbian
learning with a large variety of di?erent problem-adapted design choices such as a
data-optimum topology similarity measure or learning mode. In medicine biology and medical
bioinformatics more and more data arise from clinical measurements such as EEG or fMRI studies
for monitoring brain activity mass spectrometry data for the detection of proteins peptides
and composites or microarray pro?les for the analysis of gene expressions. Typically data are
high-dimensional noisy and very hard to inspect using classic (e. g. symbolic or linear)
methods. At the same time new technologies ranging from the possibility of a very high
resolution of spectra to high-throughput screening for microarray data are rapidly developing
and carry thepromiseofane?cient cheap andautomaticgatheringoftonsofhigh-quality data with large
information potential. Thus there is a need for appropriate - chine learning methods which
help to automatically extract and interpret the relevant parts of this information and which
eventually help to enable und- standingofbiologicalsystems reliablediagnosisoffaults
andtherapyofdiseases such as cancer based on this information. Moreover these application
scenarios pose fundamental and qualitatively new challenges to the learning systems - cause of
the speci?cs of the data and learning tasks. Since these characteristics are particularly
pronounced within the medical domain but not limited to it and of principled interest this
research topic opens the way toward important new directions of algorithmic design and
accompanying theory.
