PROCEEDINGS IPMU '08
Effectiveness of Value Granulation in Machine Learning for Massively Large and Complex Domain
Zachary I Moore, Atsushi Inoue.
Considered from data analysis and
dynamic optimization view point,
computer networks are massively
large and complex data domains
where analytical computation (e.g.
statistics) is, despite its needs, often
found infeasible. In an attempt
to address the issues raised by such
domains, we are currently studying
Granular Computing, a newly
emerging paradigm, and its application
to Machine Learning. This
paper reports effectiveness of value
granulation (such as discretization
and quantization) in Machine Learning
from aspects of complexity reduction,
learning capability, and intelligent
system development.
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