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Zhao, Xunyi (author)
Dropout is one of the most popular regularization methods used in deep learning. The general form of dropout is to add random noise to the training process, limiting the complexity of the models and preventing overfitting. Evidence has shown that dropout can effectively reduce overfitting. This thesis project will show some results where dropout...
master thesis 2021
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Schaaphok, Marianne (author)
Burn injuries occur daily and can have severe physical and mental effects both in short and long term, such as disabilities due to severe skin contraction. Even though the mortality rate has decreased over the years, the need for a higher quality of life after severe burns remains. Decreasing the probability of a severe contraction is essential...
master thesis 2020
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Song, Y. (author)
In research there is often a need to choose between multiple competing models. Two popular criteria for model selection are the AIC and BIC. The AIC excels in estimating the best model for the unknown data generating process. The BIC on the other hand is consistent in finding the true model. It is clear that for model selection these two...
bachelor thesis 2020
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Lubbers, J.C.H. (author)
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bachelor thesis 2020
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Kingma, Friso (author)
Overfitting is a common problem when learning models from noisy observational data. This problem is especially present in very flexible models, such as Neural Networks, which can easily fit to spurious patterns in the data that are not indicative of true underlying patterns. One technique that conquers the problem of overfitting is Bagging, an...
bachelor thesis 2017
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