Global ETD Search
Search theses and dissertations gathered from participating repositories worldwide. Every result links back to the library that holds it. No account is needed.
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Showing 1 to 20 of 21 for “"Maschinelles Lernen"”.
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Programming abstractions, compilation, and execution techniques for massively parallel data analysis
… Algorithmen aus den Bereichen Data Mining und Maschinelles Lernen hinzu, um versteckte Muster in den Daten zu erkennen, oder Vorhersagemodelle zu trainieren. Mit zunehmender Datenmenge und Komplexität der Analysen wird jedoch eine neue Generation von Systemen benötigt, die diese Kombination aus …
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Quantifying Dislocation Microstructures
In this work, we reconstructed and full characterized a dislocation microstructure that formed during an in situ micro-cantilever beam experiment. Based on this information, we were then able to infer how the dislocations propagated from the notch into the specimen. We propose using the so-called …
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Behavior-specific proprioception models for robotic force estimation
Robots that support humans in physically demanding tasks require accurate force sensing capabilities. A common way to achieve this is by monitoring the interaction with the environment directly with dedicated force sensors. Major drawbacks of such special purpose sensors are the increased costs and …
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Natural Audio Data Augmentation Techniques
… Augmentation bekannt ist und dazu dient, ein maschinelles Lernmodell zu trainieren und zu bewerten, wird seit langem angewendet und erforscht. Diese Arbeit befasst sich mit einem Problem der Data Augmentation im Bereich der Audioverarbeitung. Die Anwendung von Techniken zur Data Augmentation …
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Prediction with Mixture Models
Learning a model for the relationship between the attributes and the annotated labels of data examples serves two purposes. Firstly, it enables the prediction of the label for examples without annotation. Secondly, the parameters of the model can provide useful insights into the structure of the …
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Investigations on discriminative training criteria
In this work, a framework for efficient discriminative training and modeling is developed and implemented for both small and large vocabulary continuous speech recognition. Special attention will be directed to the comparison and formalization of varying discriminative training criteria and …
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Discriminative Classification Models for Internet Security
Services that operate over the Internet are under constant threat of being exposed to fraudulent use. Maintaining good user experience for legitimate users often requires the classification of entities as malicious or legitimate in order to initiate countermeasures. As an example, inbound email …
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Learning under differing training and test distributions
One of the main problems in machine learning is to train a predictive model from training data and to make predictions on test data. Most predictive models are constructed under the assumption that the training data is governed by the exact same distribution which the model will later be exposed …
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Active evaluation of predictive models
The field of machine learning studies algorithms that infer predictive models from data. Predictive models are applicable for many practical tasks such as spam filtering, face and handwritten digit recognition, and personalized product recommendation. In general, they are used to predict a target …
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Prediction games : machine learning in the presence of an adversary
In many applications one is faced with the problem of inferring some functional relation between input and output variables from given data. Consider, for instance, the task of email spam filtering where one seeks to find a model which automatically assigns new, previously unseen emails to class …
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Hierarchical Multiclass Topic Modelling with Prior Knowledge
Eine neue Multi-Label-Dokument-Klassifizierungstechnik namens CascadeLDA wird in dieser Arbeit eingeführt. Statt sich auf diskriminierende Modellierungstechniken zu konzentrieren, erweitert CascadeLDA ein generatives Basismodell durch die Einbeziehung von zwei Arten von Vorinformationen. Erstens …
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Modeling for part-based visual object detection based on local features
Today, automatic object detection in image data is usually performed using machine-learning approaches relying on a holistic object model and the sliding window principle. A major concern with holistic object detection is the insufficient tolerance to deformation, partial occlusion, and rotation. …
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Statistical methods in natural language understanding and spoken dialogue systems
Modern automatic spoken dialogue systems cover a wide range of applications. There are systems for hotel reservations, restaurant guides, systems for travel and timetable information, as well as systems for automatic telephone-banking services. Building the different components of a spoken dialogue …
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Discriminative training and acoustic modeling for automatic speech recognition
Discriminative training has become an important means for estimating model parameters in many statistical pattern recognition tasks. While standard learning methods based on the Maximum Likelihood criterion aim at optimizing model parameters only class individually, discriminative approaches …
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Analyse von Translationsstarts in prokaryotischen Genomen mit Methoden des Maschinellen Lernens
… zwei Verfahren aus dem Bereich des Maschinellen Lernens zur Verbesserung der Annotation prokaryotischer Genome, vorgestellt: Der Oligo-Kern-Algorithmus, ein überwachtes Verfahren zur Analyse von Signalen in biologischen Sequenzen und TICO (Translation Initiation site COrrection), ein Programm zur …
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A log-linear discriminative modeling framework for speech recognition
Conventional speech recognition systems are based on Gaussian hidden Markov models (HMMs).Discriminative techniques such as log-linear modeling have been investigated in speech recognition only recently. This thesis establishes a log-linear modeling framework in the context of discriminative …
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Learning communicating and nondeterministic automata
The results of this dissertation are two-fold. On the one hand, inductive learning techniques are extended and two new inference algorithms for inferring nondeterministic, and universal, respectively, finite-state automata are presented. On the other hand, certain learning techniques are employed …
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