Clausthal University of Technology, 2015. — 156 p. Data Restructuring as formal Preprocessing for Machine Learning with Neural Networks Artificial neural networks are used in the field of machine learning to build functions that emulate expert knowledge. Feedforward networks can map between data with fixed structure, recurrent networks can emulate sequential data such as time...
University of Bonn, 2015. — 167 p. In drug discovery, domain experts from different fields such as medicinal chemistry, biology, and computer science often collaborate to develop novel pharmaceutical agents. Computational models developed in this process must be correct and reliable, but at the same time interpretable. Their findings have to be accessible by experts from other...
University of Augsburg, 2018. — 227 p. The research area of Social Signal Processing paves the way for conversational companions, such as virtual agents or social robots, to become aware of nuances in our behaviours and implicit messages that come along with them. For machines to understand and interpret such behavioural cues, the state-of-the-art procedure is the application...
Dissertation, Carnegie Mellon University, 2001, -147 p. The purpose of this work is to introduce and experimentally validate a framework, based on statistical machine learning, for handling a broad range of problems in information retrieval (IR). Probably the most important single component of this framework is a parametric statistical model of word relatedness. A longstanding...
University of Duisburg-Essen, 2018. — 184 p. Common electricity meters measure only the overall energy consumption. To assist in energy savings and to enrich smart home applications with energy data, a detailed breakdown is necessary. Non-Intrusive Load Monitoring (NILM) analyzes the overall electrical signal and separates it into its components, by identifying device specific...
Technical University of Berlin, 2018 - 130 c. The popularity of the World Wide Web and its ubiquitous global online services have led to unprecedented amounts of available data. Novel distributed data processing systems have been developed in order to scale out computations and analysis to such massive data set sizes. These "Big Data Analytics" systems are also popular choices...
University of Bonn, 2005. — 155 p. Machine learning is an area of research concerned with the construction of algorithms which are able to learn from examples. Among such algorithms, so-called kernel methods form an important family of algorithms which have proven to be powerful and versatile for a large number of problem areas. Central to these approaches is the kernel matrix...
University of Duisburg-Essen, 2016. — 280 p. The classification problem is an important part of machine learning and occurs in many application fields like image-based object recognition or indus-trial quality inspection. In the ideal case, only a training dataset consistingof feature data and true class labels has to be obtained to learn the con-nection between features and...
Technical University of Munich, 2009. — 331 p. Recommendation systems − based on the functionalities clustering, classification, and prediction − automate information processing steps such as the classification of artifacts and assist the user in decision-making processes. Many of these systems are realized by symbolic methods such as association rule mining or by rule-based...
Technical University of Berlin, 2016. — 116 p. Nowadays, societies worldwide are confronted with an epidemic increase of incidence and prevalence of chronic diseases with dramatic consequences for affected individualsand considerable expenditure for health care systems. Many of the frequent chronic diseases, e.g. cardiovasculardiseases, diabetesmellitus type two...
University of Freiburg, 2015. — 161 p. Text Mining approaches cover a range of methods to extract information from usually unstructured literature resources. The largest freely available repository to search for this information is PubMed. Within the amount of different software solutions to gain knowledge from texts, the newly developed software library PubMed2Go provides a...
Tel Aviv University, 2014. — 174 p. Online decision making and learning occur in a great variety of scenarios. The decisions involved may consist of stock trading, ad placement, route planning, picking a heuristic, or making a move in a game. Such scenarios vary also in the complexity of the environment or the opponent, the available feedback, and the nature of possible...
Free University of Berlin, 2014. — 261 p. Parsing is a step for understanding a natural language to find out about the words and their grammatical relations in a sentence. Statistical parsers require a set of annotated data, called a treebank, to learn the grammar of a language and apply the learnt model on new, unseen data. This set of annotated data is not available for all...
University of Oldenburg, 2016. — 170 p. For a sustainable integration of wind power into the electricity grid, precise and robust predictions are required. Machine learning methods can be used as purely data-driven, spatio-temporal prediction models that yield better results than traditional physical models based on weather simulations. The objectives of this thesis are the...
ETH Zürich, 2011. — 206 p. Convex optimization is at the core of many of today’s analysis tools for large datasets, and in particular machine learning methods. In this thsis we will study the general setting of optimizing (minimizing) a convex function over a compact convex domain.
Humboldt University of Berlin, 2016. — 183 p. Repeated measures obtained from multiple individuals are of crucial importance for developmental research. Examples of repeated measures obtained from multiple individuals include longitudinal panel and electroencephalography (EEG) data. In this thesis, I develop a novel analysis approach based on machine learning methods for each...
University of Münster, 2017. — 54 p. Nowadays, numerous real-world workflows become more and more formalized and structured. One of the advantages of such formal processes is their accessibility for optimization. Even problems without an exact mathematical representation, i.e., so-called black-box problems, can be optimized. Unfortunately, people tend to make rather poor...
University of Tübingen, 2017. — 155 p. In this thesis I make some contributions to the development of machine learning in a setting of ordinal distance information. A setting of ordinal distance information or ordinal data for short refers to the following scenario: The objects of interest are elements of a set X that is equipped with a dissimilarity function i, which...
RWTH Aachen University, 2013. — 355 p. Robotic applications more and more expand into unstructured terrains. The new applications require detailed automatically generated models of the environment. Semantic world models map the environment and the semantic meaning of the objects in a virtual model. These models and their connection to the real world allow for precise navigation...
Technical University of Darmstadt, 2014. — 149 p. Robotics as a technology has an incredible potential for improving our everyday lives. Robots could perform household chores, such as cleaning, cooking, and gardening, in order to give us more time for other pursuits. Robots could also be used to perform tasks in hazardous environments, such as turning off a valve in an...
Technical University of Munich, 2012. — 177 p. Learning of Bayesian network structures is a NP-hard nonlinear combinatorial optimisation problem. This problem can be transformed into a linear problem but in exponential dimension using the newly introduced characteristic imsets which are combinatorial representatives. These 0/1-vectors enable us to obtain theoretical results and...
Technical University of Berlin, 2010. — 108 p. Due to some drawbacks, mainly because of semantic issues such as synonymy and polysemy, people consider some approaches to improve the performance of full-text indexing. The alternative approaches include latent semantic indexing, keyword indexing, social indexing (web 2.0) and linked data-based indexing (semantic web). The aim of...
Otto von Guericke University Magdeburg, 2009. — 140 p. Today, machine learning methods are successfully deployed in a wide range of applications. A multitude of different learning algorithms has been developed in order to solve classification and regression problems. These common machine learning approaches are regarded with suspicion by domain experts in safety-related...
Humboldt University of Berlin, 2015. — 159 p. The global dimension of urbanization constitutes a great environmental challenge for the 21st century. Remote sensing is a valuable Earth observation tool, which helps to better understand this process and its ecological implications. The focus of this work was to quantify urban land cover by means of machine learning and imaging...
University of Tübingen, 2007. — 124 p. The analysis of data from simulations and experiments in the development phase and measurements during mass production plays a crucial role in modern manufacturing: Experiments and simulations are performed during the development phase to ensure the design's fitness for mass production. During production, a large number of measurements in...
University of Hamburg, 2019. — 170 p. In this thesis, single-channel speech enhancement algorithms that either process the signal captured by a single microphone or the output of a spatial filtering algorithm are considered. The aim of this thesis is to increase the robustness of machine-learning (ML)-based and non-ML-based single-channel speech enhancement algorithms by...
Technical University of Berlin, 2009. — 151 p. Misuse detection as employed in current network security products relies on the timely generation and distribution of so called attack signatures. While appropriate signatures are available for the majority of known attacks, misuse detection fails to protect from novel and unknown threats, such as zero-day exploits and worm...
University of Stuttgart, 2017. — 194 p. Tiny feature sizes in deep submicron technologies pose both a yield and reliability threat. Imperfections in the manufacturing process may introduce systematic defects, especially as the first devices are produced when the process is not yet mature. The identification and correction of systematic process problems calls for efficient test...
Technical University of Munich, 2012. — 163 p. In this thesis, we investigate machine learning methods for human motion analysis. We introduce algorithms for human pose estimation and activity recognition that do not rely on classical cameras and that can cope with noisy and incomplete input data. We propose methods that capture human movements using body-worn inertial sensors...
University of Freiburg, 2008. — 142 p. The ever increasing amount of digital information has created a need for effective information retrieval systems. As it is said, information which cannot be found easily is as good as lost. As information comes in various formats and types, their retrieval mechanisms also need to differ correspondingly. In this work, we deal with the task...
University of Kaiserslautern, 2015. — 189 p. In this dissertation, we discuss how to price American-style options. Our aim is to study and improve the regression-based Monte Carlo methods. In order to have good benchmarks to compare with them, we also study the tree methods. In the second chapter, we investigate the tree methods specifically. We do research firstly within the...
Technical University of Berlin, 2008. — 170 p. With the development of novel sequencing technologies, the way has been paved for cost efficient, high-throughput whole genome sequencing. In the year 2008 alone, about 250 genomes will have been sequenced. It is self-evident that the handling of this wealth of data requires efficient and accurate computational methods for sequence...
Saarland University, 2009. — 134 p. Machine learning requires the use of prior assumptions which can be encoded into learning algorithms via regularisation techniques. In this thesis, we examine in three examples how suitable regularisation criteria can be formulated, what their meaning is, and how they lead to efficient machine learning algorithms. Firstly, we describe a joint...
University of Konstanz, 2018. — 139 p. Machine learning is ubiquitous in everyday life; techniques from the area of automated data analysis are used in various application scenarios, ranging from recommendations for movies over routes to drive to automated analysis of data in critical domains. To make appropriate use of such techniques, a calibration between human trust and...
Ludwig Maximilian University of Munich, 2008. — 198 p. Recursive partitioning methods from machine learning are being widely applied in many scientific fields such as, e.g., genetics and bioinformatics. The present work is concerned with the two main problems that arise in recursive partitioning, instability and biased variable selection, from a statistical point of view. With...
Otto von Guericke University Magdeburg, 2010. — 230 p. The classification of complex patterns is one of the most impressive cognitive achievements of the human brain. Humans have the ability to recognize a complex image, like for example that of a known person, and to distinguish it from other objects within half a second. While for a solution of this task the brain has access...
Technical University of Berlin, 2014. — 246 p. In this thesis, we present a general framework to improve the automatic mapping of concurrent applications to parallel architectures and we define its instantiation for mapping MPI programs to processor networks. For scheduling the parallelly executable tasks of applications and for their allocation to the processing elements of...
Technical University of Darmstadt, 2016. — 132 p. The ability of robots to perform tasks in human environments has largely been limited to rather simple and specific tasks, such as lawn mowing and vacuum cleaning. As such, current robots are far away from the robot butlers, assistants, and housekeepers that are depicted in science fiction movies. Part of this gap can be...
Technical University of Berlin, 2017. — 117 p. Advancing understanding and interpretation of machine learning algorithms has recently been receiving much attention. Although classification systems achieve high prediction accuracies and are used across a wide spectrum of academic fields, they act like a black-box and provide little or no reasoning information about their...
University of Bremen, 2017. — 153 p. Machine learning is a powerful tool for making predictions and has been widely used for solving various classification problems in last decades. As one of important applications of machine learning, gait classification focuses on distinguishing different gait patterns by investigating the quality of gait of individuals and categorizing them...
Technical University of Darmstadt, 2009. — 148 p. Many applications of Technology Enhanced Learning are based on strong assumptions: Knowledge needs to be standardized, structured and most of all externalized into learning material that preferably is annotated with meta-data for efficient re-use. A vast body of valuable knowledge does not meet these assumptions, including...
University of Mannheim, 2018. — 163 p. In a time in which computing power has never been cheaper and the possibilities of extracting knowledge from data seem ever-increasing, the idea of doing this while protecting the user’s privacy seems too good to be true. However, with the introduction of the first Fully Homomorphic Encryption scheme in 2009, we now have at our disposal a...
University of Hildesheim, 2017. — 180 p. Automating machine learning by providing techniques that autonomously find the best algorithm, hyperparameter configuration and preprocessing is helpful for both researchers and practitioners. Therefore, it is not surprising that automated machine learning has become a very interesting field of research. Bayesian optimization has proven...
Technical University of Munich, 2013. — 263 p. Intelligent automatic human behavior analysis is an essential precondition for conversational agent systems that aim to enable natural, emotionally sensitive human-computer interaction. This thesis focuses on automatic verbal and non-verbal behavior analysis and introduces novel speech processing and machine learning architectures...
Technical University of Munich, 2017. — 153 p. We present the state-of-art study of a recent emerging research area named as Adversarial Machine Learning, it investigates the vulnerabilities of current learning algorithms from the perspective of an adversary. We show that several state-of-art learning systems are intrinsically vulnerable under carefully designed adversarial...
University of Tübingen, 2017. — 148 p. Computer malware is a well-known threat in security which, despite the enormous time and effort invested in fighting it, is today more prevalent than ever. Recent years have brought a surge in one particular type: malware embedded in non-executable file formats, e.g., PDF, SWF and various office file formats. The result has been a massive...
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