Publicaties

Adaptive learning systems aim to provide learning items tailored to the behavior and needs of individual learners. However, one of the outstanding challenges in adaptive item selection is that often the corresponding systems do not have information on initial ability levels of new learners entering a learning environment. Thus, the proficiency of those new learners is very difficult to be predicted. This heavily impairs the quality of ...

We propose a general framework for designing machine learning models that deal with constructing complex structures in the output space. The goal is to provide an abstraction layer to easily represent and design constructive learning models. The learning approach is based on generalized linear training techniques, and exploits techniques from combinatorial optimization to deal with the complexity of the underlying inference required in this type ...

Code smells represent poor implementation choices performed by developers when enhancing source code. Their negative impact on source code maintainability and comprehen-sibility has been widely shown in the past and several techniques to automatically detect them have been devised. Most of these techniques are based on heuristics, namely they compute a set of code metrics and combine them by creating detection rules; while they have a reasonable ...

Despite the popularity of machine learning and data mining today, it remains challenging to develop applications and software that incorporates machine learning or data mining techniques. This is because machine learning and data mining have focussed on developing high-performance algorithms for solving particular tasks rather than on developing general principles and techniques. I propose to alleviate these problems by applying the constraint ...

Machine learning is a subdiscipline within artificial intelligence that focuses on algorithms that allow computers to learn solving a (complex) problem from existing data. This ability can be used to generate a solution to a particularly intractable problem, given that enough data is available to train and subsequently evaluate an algorithm on. Since mass spectrometry based proteomics has no shortage of complex problems, and since publicly ...

© 2018, The Author(s). How well can machine learning predict the outcome of a soccer game, given the most commonly and freely available match data? To help answer this question and to facilitate machine learning research in soccer, we have developed the Open International Soccer Database. Version v1.0 of the Database contains essential information from 216,743 league soccer matches from 52 leagues in 35 countries. The earliest entries in the ...

PURPOSE: Machine learning may contribute to understanding the relationship between the external load and internal load in professional soccer. Therefore, the relationship between external load indicators (ELIs) and the rating of perceived exertion (RPE) was examined using machine learning techniques on a group and individual level. METHODS: Training data were collected from 38 professional soccer players over 2 seasons. The external load was ...

Purpose: To study, from a machine learning perspective, the performance of several machine learning classifiers that use texture analysis features extracted from soft-tissue tumors in nonenhanced T1-MRI images to discriminate between malignant and benign tumors. Materials and Methods: Texture analysis features were extracted from the tumor regions from T1-MRI images of clinically proven cases of 49 malignant and 86 benign soft-tissue tumors. ...