Acquiring
information properly through machine learning requires familiarity with the
available algorithms and understanding how they work and how to address the
given the problem in the best possible way.
However,
even for machine-learning experts in specific industrial fields, in order to
predict and acquire information properly in different industrial fields, it is
necessary to attempt several instances of trial and error to succeed with the
application of machine learning. For non-experts, it is much more difficult to make
accurate predictions through machine learning.
In
this paper, we propose an autonomic machine learning platform which provides
the decision factors to be made during the developing of machine learning
applications. In the proposed autonomic machine learning platform, machine
learning processes are automated based on the specification of autonomic
levels.
This
autonomic machine learning platform can be used to derive a high-quality
learning result by minimizing experts’ interventions and reducing the number of
design selections that require expert knowledge and intuition. We also
demonstrate that the proposed autonomic machine learning platform is suitable
for smart cities which typically require considerable amounts of security
sensitive information.
Conference: “Future of Artificial Intelligence, Automation & Robotics”
Important Dates: October 22-23, 2020
Venue: Rome, Italy
Important Dates: October 22-23, 2020
Venue: Rome, Italy
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