Search Results (9)
Autonomous generation of robot behaviour for effective task execution
The pursuit of autonomous robot learning is aimed at enabling robots to seamlessly integrate into our daily lives, enhancing our efficiency and convenience. On the other hand, in the industrial sector, autonomous robot learning plays a vital role in keeping up with the rapidly evolving market demands. As products and …
Investigating the early molecular events following exposure of lung cells to nanoparticles using advanced optical microscopies
The potential toxicity of nanoparticles in our environment and consumer products is currently determined by costly and timely animal-based testing, which limits the rate of nanoparticle testing, causing a desperate need for alternative testing strategies. A promising alternative – mechanism-based prediction – employs a set of high-throughput cell-based tests that …
Classification of wireless links using machine learning techniques
Due to the nature of the wireless transmission medium, wireless communications are characterised by notably larger losses of data packets than wired communications. The quality of wireless links is highly dependent on channel variations, interference and even transceiver imperfections. Such link uncertainty instigated the development of numerous techniques that can …
Complex nodes in trees for structured output prediction
In this thesis, we integrate complex nodes into predictive clustering trees (PCTs). PCTs are well-established machine learning models that are very flexible in terms of the machine learning tasks that they can address, including structured output prediction and semisupervised learning. Like standard decision trees, they are learned with a greedy …
Considering autocorrelation in predictive models
Most machine learning, data mining and statistical methods rely on the assumption that the analyzed data are independent and identically distributed (i.i.d.). More specifically, the individual examples included in the training data are assumed to be drawn independently from each other from the same probability distribution. However, cases where this …
A Machine Learning Approach to Polynomial Regression
In the thesis, we address the task of polynomial regression, i.e., inducing regression models based on polynomial equations, from data. We aim at improving and extending the existing approaches to learning polynomial regression models in several directions. First, we improve the existing methods for addressing the issue of over-fitting and …
A Modular Ontology of Data Mining
The domain of data mining (DM) deals with analyzing different types of data. The data typically used in data mining is in the format of a single table, with primitive datatypes as attributes. However, structured (complex) data, such as graphs, sequences, networks, text, image, multimedia and relational data, are receiving …
Semi-automatic ontology construction
This thesis addresses the task of formalizing and implementing the process of semi-automatic ontology construction. We propose a theoretical framework for formalizing the ontology construction process. The process is described as a sequence of operators applied to the ontology. Several types of common operators are identified and each type is …
Ensembles for predicting structured outputs
In this thesis, we address the task of learning models for predicting structured outputs, which take as input a tuple of attribute values and produce as output a structured object. In contrast to classification and regression, where the output is a single scalar value, in our case the output is …