The ultimate goal in machine learning is to construct a model function that has generalization capability for unseen dataset, based on given training dataset. If the too much expressibility power, then it may overfit data and as result lose capability. To avoid such overfitting issue, several techniques have been developed classical regime, dropout one effective method. This paper proposes stra...

A search engine strikes a balance between effectiveness and efficiency to retrieve the best documents in scalable way. Recent deep learning-based ranker methods are proving be effective improving state-of-the-art relevancy metrics. However, as opposed index-based retrieval methods, neural rankers like bidirectional encoder representations from transformers (BERT) do not scale large datasets. In...

The application of feed forward back propagation artificial neural networks with one hidden layer (ANN) to perform the equivalent of multiple linear regression (MLR) has been examined using artificial structured data sets and real literature data. The predictive ability of the networks has been estimated using a training/ test set protocol. The results have shown advantages of ANN over MLR anal...

Process models discovered using process mining tend to be complex and have problems balancing between overfitting and underfitting. Overfitting models are not general enough while underfitting models allow for too much behavior. This paper presents a post-processing approach to simplify discovered process models while controlling the balance between overfitting and underfitting. The discovered ...

In this report, several experiments have been conducted on a spam data set with Logistic Regression based on Gradient Descent approach. First, the overfitting effect is shown with basic settings (vanilla version). Then Stochastic Gradient Descent and 2-Norm Regularization techniques are both implemented with demonstration of the benefits of these two methods in preventing overfitting. Besides, ...