Boosted-Linear Regression Neural Network
Martha G Smons (Marthasimons)
on
March 9, 2021
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We present a framework that improves on a
commonly used method for training
regression models for continuous inputs.
Our approach is inspired from data mining,
and utilizes a linear model as the basis.
This model is trained by considering the
input over the input matrix. After
evaluating the model's performance, a
regression classifier is first trained to
evaluate the model's performance over the
input matrix. The classification accuracy
of the classifier is then compared with
other factors such as the training time
and the class itself. The proposed method
is applied to the case of binary
classification data which is not in the
input matrix or has a complex structure.
Finally, validation is performed on the
feature vectors of the binary
classification data which may or cannot be
represented by the binary classification
data. The results provide an excellent
measure of the importance of the binary
classification data from the
classification performance, and
demonstrate the benefits of our approach
on real-world datasets where no binary
data is available.
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An Online Strategy for Online Group Time-
Sensitive Tournaments
We consider the problem of online group
time sensitive tournaments which is
challenging due to the large number of
participants, the high risk of injuries,
and the fact that the tournament is time
sensitive. Many online tournaments involve
participants coming together and are often
conducted under a time-sensitive scenario,
where the tournament rules the
participants' decision. However, the
tournament rules themselves are often not
clear, especially for different rules that
are not clear. We present a novel way to
compute rules that are easy to find even
with very large data sets. This can
therefore help the participants to
understand the rules, or at least better
understand their understanding.
Experiments have shown that the proposed
framework is very effective when tested on
an online tournament of tournaments with a
large number of participants. For example,
in tournaments where participants come
together for less than 10 rounds, our
framework makes it possible to obtain
rules for the average player in an average
time, which can be used for decision
making.
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