Predictive Modeling Using Sparse Logistic Regression with Applications

In this thesis, sparse logistic regression models are applied in a set of real world machine learning applications. The studied cases include supervised image segmentation, cancer diagnosis, and MEG data clas-sification. Image segmentation is applied both in component detection in inkjet printed electronics manufacturing and in cell detection from microscope images. The results indicate that a simple linear classifi-cation method such as logistic regression often outperforms more so-phisticated methods. Further, it is shown that the interpretability of the linear model offers great advantage in many applications. Model validation and automatic feature selection by means of `1 regularized parameter estimation have a significant role in this thesis. It is shown that a combination of a careful model assessment scheme and auto-matic feature selection by means of logistic regression model and coef-ficient regularization create a powerful, yet simple and practical, tool chain for applications of supervised learning and classification