Active Learning by Discrepancy Minimization: A Comparison of Active Learning Methods Motivated by Generalization Bounds

In many settings in practice it is expensive to obtain labeled data while unlabeled data is abundant. This is problematic if one wants to train accurate (supervised) predictive models. The main idea behind active learning is that models can perform better with less labeled data, if the model may choose the data from which it learns. Active...

Where will you comment next? Exploiting comments for personalized recommendations

Since the advent of Web 2.0, users have not only increasingly created content, but also contributed reactions to content in the form of comments. Comments are challenging to analyze due to their short lengths and informal style, meaning that any individual comment provides very little data to work with and is highly variable. However, comments...

Learning about the opponent in automated bilateral negotiation: A comprehensive survey of opponent modeling techniques

A negotiation between agents is typically an incomplete information game, where the agents initially do not know their opponent’s preferences or strategy. This poses a challenge, as efficient and effective negotiation requires the bidding agent to take the other’s wishes and future behavior into account when deciding on a proposal. Therefore, in...

High-Speed Object Detection: Design, Study and Implementation of a Detection Framework using Channel Features and Boosting

In this thesis we design, implement and study a high-speed object detection framework. Our baseline detector uses integral channel features as object representation and AdaBoost as supervised learning algorithm. We suggest the implementation of two approximation techniques for speeding up the baseline detector and show their effectiveness by...

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...

Sequence-based learning algorithms for understanding and improving protein characteristics

The development of high-throughput measurement techniques resulted in rapidlyincreasing amounts of biological data, which made computational methodsessential for biological research. Hence, the field of bioinformatics emergedthat since plays an important role in storing, making accessible, integrating,and analysing different types of biological...

VizBin: An application for reference-independent visualization and human-augmented binning of metagenomic data

Background: Metagenomics is limited in its ability to link distinct microbial populations to genetic potential due to a current lack of representative isolate genome sequences. Reference-independent approaches, which exploit for example inherent genomic signatures for the clustering of metagenomic fragments (binning), offer the prospect to...

Using the Multiple Instance Learning framework to address differential regulation

Cell differentiation is a natural process occurring in all higher organisms, since the early fetal stage of life. It is, also, a part of disease – such as cancer – as the cell cycle becomes deregulated and cells behave differently compared to healthy ones. Differentiation occurs although the genome of all cells is identical across all cell types...

Advancing the Relevance Criteria for Video Search and Visual Summarization

To facilitate finding of relevant information in ever-growing multimedia collections, a number of multimedia information retrieval solutions have been proposed over the past years. The essential element of any such solution is the relevance criterion deployed to select or rank the items from a multimedia collection to be presented to the user....

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....

Sample reusability in importance-weighted active learning

Recent advances in importance-weighted active learning solve many of the problems of traditional active learning strategies. But does importance-weighted active learning also produce a reusable sample selection? This thesis explains why reusability can be a problem, how importance-weighted active learning removes some of the barriers to...