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...

Kernel based LTI and LPV subspace identification: A synergy between machine learning and system identification methods

System identification is the art of constructing mathematical models from observed data. It is a well established field with a history of over 40 years, characterized by a rich theoretical background, while it has proven its worth in many real life applications. On the other hand, Gaussian processes form a specific category of kernel based...

Real-time estimation of surgical procedure duration

Each surgical procedure varies in length. There’s a need for dynamic planning to have an optimal patient flow from their beds to the operating room. This paper proposes an adaptive system that predicts the end of a gallbladder removal surgery 25 minutes in advance with the use of Support Vector Machines based on the activations of the...

Anomaly detection for internet banking using supervised learning on high dimensional data

Nowadays, a high number of transactions are performed via internet banking. Rabobank processes more than 10 million transactions per day. Most of these transactions are (part of) normal behaviour. On the other hand, some transactions are considered to be out of the ordinary. These anomalous events occur relatively infrequently (less than 10 per...

Classification of valence using facial expressions of TV-viewers

Emotion has been shown to have a large impact on our interactions with people and devices. In our daily lives, however, these emotions are not taken into account when working with our computers and other machines. If our devices could pick up on social cues, for instance in relation to disinterest, the usability of various systems could be...

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...

Interpretable classification of tumours through multiple instance learning and somatic mutations

Next generation sequencing is brought into the clinic. Screening of disease associated genes will aid the diagnosis of disorders with a genetic component. The diagnosis of cancer is of particular interest due to its variety and prevalence. The obtained mutations provide clues about underlying biological properties that could be used for...

An Exploratory Study on Authorship Verification Models for Forensic Purpose

Authorship verification is one subfield of authorship analysis. However, the majority of the research in the field of authorship analysis is on the authorship identification problem. The authorship verification problem has received less attention than the authorship identification problem. Thus, there is a demand for a study on the authorship...

Event correlation for detecting advanced multi-stage cyber-attacks

Rapidly evolving IT infrastructures bring beneficial effects to society and promote information sharing and use. However, vulnerabilities create opportunities for hostile users to perform malicious activities and IT security has gradually turned into a critical research area for organizations and governments. Processes of decision making in...

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...

Evaluating the Quality of Opponent Models in Automated Bilateral Negotiations

Automated negotiation agents are agents that interact in an environment for the settlement of a mutual concern. An important factor influencing the performance of a negotiation agent is how it takes the opponent into account. The main challenge in this aspect, is that opponents typically hide private information to avoid exploitation. In such a...

Towards a roadmap for development of intelligent data analysis based cyber attack detection systems

Cyber-attacks against companies and governments are seeing an increase in complexity and persistence. These more complex attacks are aimed at penetrating corporate and government networks to obtain classified information. Common intrusion detection methods lack in their ability to detect such complex attacks. A framework is proposed to relate...