Background and Purpose: Nowadays, breast cancer is reported as one of the most common cancers amongst women. Early detection of the cancer type is essential to aid in informing subsequent treatments. The newest proposed breast cancer detectors are based on deep learning. Most of these works focus on large-datasets and are not developed for small datasets. Although the large datasets might lead ...

Variational and ensemble methods have been developed separately by various research and development groups and each brings its own benefits to data assimilation. In the last decade or so, various ways have been developed to combine these methods, especially with the aims of improving the background-error covariance matrices and of improving efficiency. The field has become confusing, even to ma...

A major issue for developing post-processing methods for NWP forecasting systems is the need to obtain complete training datasets. Without a complete dataset, it can become difficult, if not impossible, to train and verify statistical post-processing techniques, including ensemble consensus forecasting schemes. In addition, when ensemble forecast data are missing, the real-time use of the conse...

Ensemble methods allow to improve the accuracy of classification methods. This work considers the application of one of these methods, named Rotation-based, when the classifiers to combine are RBF Networks. This ensemble method, for each member of the ensemble, transforms the data set using a pseudo-random rotation of the axis. Then the classifier is constructed using this rotation data. The re...

Material decomposition facilitates the differentiation of different materials in X-ray imaging. As an alternative to the previous empirical material decomposition methods, we performed material decomposition using ensemble learning methods in this work. Three representative ensemble methods with two decision trees as the base learning algorithms were implemented to perform material decompositio...

Owing to the development of DNA microarray technologies, it is possible to get thousands of expression levels of genes at once. If we make the effective classification system with such acquired data, we can predict the class of new sample, whether it is normal or patient. For the classification system, we can use many feature selection methods and classifiers, but a method cannot be superior to...

The main focus of this thesis concerns the further developments in the areas of ensemble and constrained clustering. The goal of the proposed methods is to address clustering problems, in which the optimal clustering method is unknown. Additionally, by means of pairwise linkage constraints, it is possible to aggregate extra information to the clustering framework. Part I investigates the concep...

Ensemble methods are popular learning methods that are usually able to increase the predictive accuracy of a classifier. On the other hand, this comes at the cost of interpretability, and insight in the decision process of an ensemble is hard to obtain. This is a major reason why ensemble methods have not been extensively used in the setting of inductive logic programming. In this paper we aim ...

Ensemble classification methods have been shown to produce more accurate predictions than the base component models (Bauer and Kohavi 1999). Due to their effectiveness, ensemble approaches have been applied in a wide range of domains to improve classification. The expected prediction error of classification models can be decomposed into bias and variance (Friedman 1997). Ensemble methods that i...