Ludwig Maximilian University of Munich, 2018. — 181 p. Knowledge bases store structured information about entities or concepts of the world and can be used in various applications, such as information retrieval or question answering. A major drawback of existing knowledge bases is their incompleteness. In this thesis, we explore deep learning methods for automatically...
IOS Press, 2010, -225 p. Knowledge Discovery from data streams is one of the most relevant challenges that we face today. Data mining algorithms for analyzing static data sets, assuming stationary distributions, unlimited memory, and generating static models are almost obsolete for the real challenging problems we are faced nowadays. This research is not in the established data...
University of Regensburg, 2010. — 164 p. The goal of this Ph.D. thesis is to exemplify how methods to model complex systems, mainly the language of complex network science, and machine learning approaches can profit from each other. Thereby it deals with several projects arising from concrete questions to different complex systems from multiple fields of science. An...
Technical University of Munich, 2018. — 119 p. The thesis aims at obtaining: (1) efficient representation of "static movement" from high-dimensional data, (2) prediction for dynamic movements from temporal data, using machine learning. To study the behaviour of human/robot movements, new machine learning methods for movement modeling are developed in this thesis. The methods...
University of Hanover, 2015. — 178 p. The Internet and more specifically Web 2.0 is a promoter and enhancer of collective intelligence as it allows people to easily generate, store and retrieve information that can be shared without difficulty. Thus both the expression and exploitation of the wisdom of the crowdsare facilitated by applications relying on collective and...
RWTH Aachen University, 2015. — 279 p. Recent advances in data collecting devices and data storage systems are continuously offering cheaper possibilities for gathering and storing increasingly bigger volumes of data. Similar improvements in the processing power and data bases enabled the accessibility to a large variety of complex data. Data mining is the task of extracting...
Thesis, Center for Telematics and Information Technology, 2004, -287 p. This thesis presents three studies in the context of search technology for text. The first two studies investigate how linguistic resources can be combined with state-of-theart generative probabilistic IR models, also known as the language modeling approach to IR, in an effective and efficient way. In...
Technical University of Munich, 2017. — 164 p. Today, patient data often includes large amounts of structured information, suchas neuroimaging data, neuropsychological test results, demographic variables, etc. Human beings, however, cannot analyze so much information, at least not without the help of modern data mining and machine learning methods. Given the diverse sources of...
Thesis, Universität Karlsruhe, 2007, -296 p. Ontologies and semantic metadata can theoretically solve all problems of traditional full-text search engines. In practice, however, ontologies and semantic metadata are always imperfect. They may miss facts, contain erroneous information, and sometimes even our knowledge of the world that should be represented in the ontology is...
Dissertation, Otto-von-Guericke-Universität, Magdeburg, 2000, -163 p. In this thesis neuro-fuzzy methods for data analysis are discussed. We consider data analysis as a process that is exploratory to some extent. If a fuzzy model is to be created in a data analysis process it is important to have learning algorithms available that support this exploratory nature. This thesis...
University of Oldenburg, 2018. — 169 p. This thesis proposes an approach to build virtual sensors based on machine learning to replace broken physical sensors. These virtual sensors are trained with sensor data from the Time Series Station Spiekeroog (TSS) and the Biodiversity-Ecosystem Functioning across marine and terrestrial ecosystems (BEFmate) project in the Wadden Sea. In...
Heinrich-Heine-Universitat Dusseldorf, 2008. — 151 p. Data mining is considered as one of the most powerful technologies that participates greatly in helping companies in any industry to focus on the most important information in their data warehouses. In this book, we developed a new approach that measures the effectiveness of data mining in helping retail websites designers...
Université de Neuchâtel, 1996. — 102 p. In this dissertation, a new logical model of information retrieval is developed and evaluated experimentally. This model is built on a general technique for uncertain reasoning called probabilistic argumentation systems (PAS), in which propositional logic and probability theory are combined to represent and handle uncertain knowledge,...
University of Konstanz, 2018. — 195 p. Big data poses many facets and challenges when analyzing data, often described with the five big V’s of Volume, Variety, Velocity, Veracity, and Value. However, the most important V – Value can only be achieved when knowledge can be derived from the data. The volume of nowadays datasets make a manual investigation of all data records...
University of Kassel, 2014. — 195 p. Interconnections between various networks help the emergence of several shortcomings such as generating voluminous data flow, intimidating services to be vulnerable, and increasing the amount of suspicious connections rapidly. In addition, malware solutions and standard security gateway such as the firewall system or the URL blocker have...
Thesis, University of Cambridge, 2003, -241 p. Many data sources in our world are stochastic in nature. They may be represented by statistical models which are applied to inference tasks such as classification. Unfortunately the precise nature of data sources is often unknown and sufficiently complicated that any statistical models proposed are much simpler and to some degree...
Dissertation, Cornell University, 2002, -140 p. One goal of research in artificial intelligence is to automate tasks that currently require human expertise; this automation is important because it saves time and brings problems that were previously too large to be solved into the feasible domain. Data analysis, or the ability to identify meaningful patterns and trends in large...
Bielefeld University, 2015. — 150 p. Concepts are central to human cognition and one important type of concepts can be represented naturally with symbolic rules. The learning of such rule-based concepts from examples relies both on a process of perception, which extracts information from the presented examples, and a process of concept construction, which leads to a rule that...
Technical University of Munich, 2018. — 150 p. Motivated by the advances in communication and computation technologies, the present thesis addresses research questions related to distributed algorithm analysis and topology design in network dynamical systems. The focus is on opinion dynamics over cooperative-competitive social networks and on topology manipulation in...
Doctoral Thesis. The Royal Institute of Technology. Department of Computer and Systems Sciences. December 2003. Abstract. Hard problems force innovative approaches and attention to detail, their exploration often contributing beyond the area initially attempted. This thesis investigates the data mining process resulting in a predictor for numerical series. The series...
Dissertation. — Carnegie Mellon University, 2005. — 174 p. In traditional machine learning approaches to classification, one uses only a labeled set to train the classifier. Labeled instances however are often difficult, expensive, or time consuming to obtain, as they require the efforts of experienced human annotators. Meanwhile unlabeled data may be relatively easy to...
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