Jurica Levatic

Orcid: 0000-0003-0721-0564

According to our database1, Jurica Levatic authored at least 16 papers between 2013 and 2024.

Collaborative distances:
  • Dijkstra number2 of five.
  • Erdős number3 of four.

Timeline

Legend:

Book 
In proceedings 
Article 
PhD thesis 
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Other 

Links

On csauthors.net:

Bibliography

2024
Semi-Supervised Multi-Label Classification of Land Use/Land Cover in Remote Sensing Images With Predictive Clustering Trees and Ensembles.
IEEE Trans. Geosci. Remote. Sens., 2024

Semi-Supervised Predictive Clustering Trees for (Hierarchical) Multi-Label Classification.
Int. J. Intell. Syst., 2024

2023
CLUSplus: A decision tree-based framework for predicting structured outputs.
SoftwareX, December, 2023

2022
Semi-supervised learning for structured output prediction.
Informatica (Slovenia), 2022

2021
Exploiting partially-labeled data in learning predictive clustering trees for multi-target regression: A case study of water quality assessment in Ireland.
Ecol. Informatics, 2021

2020
Semi-supervised regression trees with application to QSAR modelling.
Expert Syst. Appl., 2020

2018
Semi-supervised trees for multi-target regression.
Inf. Sci., 2018

Machine learning for predicting thermal power consumption of the Mars Express Spacecraft.
CoRR, 2018

2017
Self-training for multi-target regression with tree ensembles.
Knowl. Based Syst., 2017

Semi-supervised classification trees.
J. Intell. Inf. Syst., 2017

Phenotype Prediction with Semi-supervised Classification Trees.
Proceedings of the New Frontiers in Mining Complex Patterns - 6th International Workshop, 2017

2015
The importance of the label hierarchy in hierarchical multi-label classification.
J. Intell. Inf. Syst., 2015

Semi-supervised learning for multi-target regression (Discussion paper).
Proceedings of the 23rd Italian Symposium on Advanced Database Systems, 2015

2014
Semi-supervised Learning for Multi-target Regression.
Proceedings of the New Frontiers in Mining Complex Patterns - Third International Workshop, 2014

2013
Semi-Supervised Learning for Quantitative Structure-Activity Modeling.
Informatica (Slovenia), 2013

The Use of the Label Hierarchy in Hierarchical Multi-label Classification Improves Performance.
Proceedings of the New Frontiers in Mining Complex Patterns, 2013


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