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Feature construction, encompassing both feature engineering, which involves the manual design of features by domain experts, and representation learning, which refers to the automated discovery of useful data representations during model construction, is a fundamental aspect of machine learning. Its goal is to transform raw data into a more suitable …

The rapid advancements in Machine Learning (ML) and Black-Box Optimization (BBO) have led to an increased reliance on benchmarking data for evaluating and comparing algorithms across diverse domain tasks. However, the effective exploitation of this data is hindered by challenges such as syntactic variability, semantic ambiguity, and lack of standardization. …

Cardiovascular diseases (CVDs) are a leading cause of mortality globally, significantly affecting patient quality of life and imposing considerable demands on healthcare systems. Chronic heart failure (CHF), a common outcome of CVDs, represents a growing health burden with an increasing incidence. Accurate and early identification of CVDs is critical for …

Automatic terminology extraction, also known as automatic term extraction (ATE), is a natural language processing (NLP) task that identifies specialized terminology from domain-specific corpora. ATE is often used for terminographic tasks (e.g., the creation of specialized dictionaries) and contributes to several complex downstream tasks (e.g., machine translation and information retrieval). …

This thesis introduces a novel machine learning methodology for automatically assigning metadata to digitized artifacts in cultural heritage. Cultural heritage is an example of a domain that requires expert labeling, with few pre-existing labeled datasets and where simply getting more labeled data is challenging. The societal importance of cultural heritage …

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 …

With the resurgence of neural network-based learning in the last decade, machine learning methods are becoming critical components of many real-life intelligent systems. However, while being able to learn effectively and at scale, such systems are often non-interpretable and unable to exploit existing symbolic background knowledge. The paradigm that offers …

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 …

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 …

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 …