1 citations · 1 across the 3 of their papers we have counts for
9 papers
P(Expression|Grammar): Probability of deriving an algebraic expression with a probabilistic context-free grammar
Urh Primožič, Ljupčo Todorovski, Matej Petković
Probabilistic context-free grammars have a long-term record of use as generative models in machine learning and symbolic regression. When used for symbolic regression, they generat…
Boosting the Performance of Quantum Annealers using Machine Learning
Jure Brence, Dragan Mihailović, Viktor Kabanov +3
Noisy intermediate-scale quantum (NISQ) devices are spearheading the second quantum revolution. Of these, quantum annealers are the only ones currently offering real world, commerc…
Explaining the Performance of Multi-label Classification Methods with Data Set Properties
Jasmin Bogatinovski, Ljupčo Todorovski, Sašo Džeroski +1
Meta learning generalizes the empirical experience with different learning tasks and holds promise for providing important empirical insight into the behaviour of machine learning…
Comprehensive Comparative Study of Multi-Label Classification Methods
Jasmin Bogatinovski, Ljupčo Todorovski, Sašo Džeroski +1
Multi-label classification (MLC) has recently received increasing interest from the machine learning community. Several studies provide reviews of methods and datasets for MLC and…
Probabilistic Grammars for Equation Discovery
Jure Brence, Ljupčo Todorovski, Sašo Džeroski
Equation discovery, also known as symbolic regression, is a type of automated modeling that discovers scientific laws, expressed in the form of equations, from observed data and ex…
Equation Discovery for Nonlinear System Identification
Nikola Simidjievski, Ljupčo Todorovski, Juš Kocijan +1
Equation discovery methods enable modelers to combine domain-specific knowledge and system identification to construct models most suitable for a selected modeling task. The method…