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similar learning tasks. The similarity measure of
learning tasks is based on relevance weights on a
common set of base features only. This avoids the
need to calculate or transfer large amounts of data
so that an efficient feature transfer is guaranteed
with minimal communication overhead. Thus, we
cannot only find accurate features by searching
them locally but also very fast by querying other
nodes or reusing the results of former runs.
We have exemplified this scenario on Nemoz,
which is a distributed media organization
framework that focuses on the application of
data mining in p2p networks. It supports users
in structuring their private media collections by
exploiting information from other peers in the
described way. It contains traditional functions
as file sharing as well as intelligent functionality,
for example classifying and clustering music files
or advanced visualization. The Nemoz framework
allows for the incorporatation of a large variety
of different data mining algorithms and coopera-
tion protocols.
The amount of media available is ever increas-
ing. Organizing personal music collection by ma-
chine learning is a key to manage this information
overload. We think that collaborative computation
will play a central role in this process.
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