The quantity of data generated increases daily, which makes it difficult to process. In the case of supervised learning, labeling training examples may represent an especially tedious and costly task. One of the aims of positive and unlabeled (PU) learning is to train a binary classifier from partially labeled data, representing a strategy for comb...
One of the biggest challenges in continual learning domains is the tendency of machine learning models to forget previously learned information over time. While overcoming this issue, the existing approaches often exploit large amounts of additional memory and apply model forgetting mitigation mechanisms which substantially prolong the training pro...
Konvolucijske nevronske mreže so vrsta nevronskih mrež, te pa spadajo pod metode strojnega učenja. Strojno učenje je vrsta umetne inteligence, kamor uvrščamo modele in algoritme za napovedovanje in analizo podatkov. Primarni namen konvolucijskih nevronskih mrež je analiziranje vizualnih podob, kot so na primer slike in video podatki. Z njihovo upor...
In many ambient-intelligence applications, including intelligent homes and cities, awareness of an inhabitant’s presence and identity is of great importance. Such an identification system should be non-intrusive and therefore seamless for the user, especially if our goal is ubiquitous and pervasive surveillance. However, due to privacy concerns and...
This study introduces two datasets for multimodal research on cognitive load inference and personality traits. Different to other datasets in Affective Computing, which disregard participants’ personality traits or focus only on emotions, stress, or cognitive load from one specific task, the participants in our experiments performed seven different...
