Dissimilarity-Based Multiple Instance Learning

Multiple instance learning (MIL) is an extension of supervised learning where the objects are represented by sets (bags) of feature vectors (instances) rather than individual feature vectors. For example, an image can be represented by a bag of instances, where each instance is a patch in that image. Only bag labels are given, however, the...

Multi-Scale Pattern Recognition for Image Classification and Segmentation

Scale is an important parameter of images. Different objects or image structures (e.g. edges and corners) can appear at different scales and each is meaningful only over a limited range of scales. Multi-scale analysis has been widely used in image processing and computer vision, serving as the basis for many high-level image analysis systems....

Constrained parameter estimation for semi-supervised learning: The case of the nearest mean classifier

A rather simple semi-supervised version of the equally simple nearest mean classifier is presented. However simple, the proposed approach is of practical interest as the nearest mean classifier remains a relevant tool in biomedical applications or other areas dealing with relatively high-dimensional feature spaces or small sample sizes. More...

Finding cancer genes in copy number data and insertional mutagenesis data

Cancer is a genetic disease. Step-wise alteration of genes that have a normal function in the cell can lead to the transformation of a healthy cell into a malignant cancer cell. Cancer genes provide several traits to the cell that allow it to become malignant. These traits have been researched for many years, and currently one knows quite well...

Database guided detection of anatomical landmark points in 3D images of the heart

Efficient Multiclass ROC Approximation by Decomposition via Confusion Matrix Perturbation Analysis

Receiver operator characteristic (ROC) analysis has become a standard tool in the design and evaluation of two-class classification problems. It allows for an analysis that incorporates all possible priors, costs, and operating points, which is important in many real problems, where conditions are often nonideal. Extending this to the multiclass...

Progressive visualization of incomplete sonar-data sets: From sea-bottom interpolation and segmentation to geometry extraction

This thesis describes a visualization pipeline for sonar profiling data that show reflections of multiple sediments in the sea bottom and that cover huge survey areas with many gaps. Visualizing such data is not trivial, because they may be noisy and because data sets may be very large. The developed techniques are: (1) Quadtree interpolation...

Learning to recognise: A study on one-class classification and active learning

The thesis treats classification problems which are undersampled or where there exist an unbalance between classes in the sampling. The thesis is divided into three parts. The first two parts treat the problem of one-class classification. In the one-class classification problem, it is assumed that only examples of one of the classes, the target...

Fuzzy Probabilistic Learning and Reasoning, Rule Induction for Decision-Support Systems in Exacting Environments