Essays about: "Regularisering"
Showing result 11 - 15 of 17 essays containing the word Regularisering.
-
11. Exploring the LASSO as a Pruning Method
University essay from Lunds universitet/Beräkningsbiologi och biologisk fysik - Genomgår omorganisationAbstract : In this study, the efficiency of various pruning algorithms were investigated, with an emphasis on regularization methods. Pruning is a method which aims to remove excess objects from a neural network. READ MORE
-
12. Solving an inverse problem for an elliptic equation using a Fourier-sine series.
University essay from Linköpings universitet/Matematiska institutionenAbstract : This work is about solving an inverse problem for an elliptic equation. An inverse problem is often ill-posed, which means that a small measurement error in data can yield a vigorously perturbed solution. Regularization is a way to make an ill-posed problem well-posed and thus solvable. READ MORE
-
13. Using LASSO regularization as a feature selection tool.
University essay from Lunds universitet/Beräkningsbiologi och biologisk fysik - Genomgår omorganisationAbstract : The subject of deep learning has become increasingly popular, especially for machine learning applications where a large number of input variables have to be processed. However, there are instances of problem solving, where a full understanding of the variables is of high importance. READ MORE
-
14. Machine learning in logistics : Increasing the performance of machine learning algorithms on two specific logistic problems
University essay from Luleå tekniska universitet/Institutionen för system- och rymdteknikAbstract : Data Ductus, a multination IT-consulting company, wants to develop an AI that monitors a logistic system and looks for errors. Once trained enough, this AI will suggest a correction and automatically right issues if they arise. READ MORE
-
15. Forecasting foreign exchange rates with large regularised factor models
University essay from KTH/Matematisk statistikAbstract : Vector autoregressive (VAR) models for time series analysis of high-dimensional data tend to suffer from overparametrisation as the number of parameters in a VAR model grows quadratically with the number of included predictors. In these cases, lower-dimensional structural assumptions are commonly imposed through factor models or regularisation. READ MORE