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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. Reserch Links: id=1814385

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