| Estimating vegetation height and canopy cover from remotely sensed data with machine learning D Stojanova, P Panov, V Gjorgjioski, A Kobler, S Džeroski Ecological Informatics 5 (4), 256-266, 2010 | 157 | 2010 |
| Analysis of time series data with predictive clustering trees S Džeroski, V Gjorgjioski, I Slavkov, J Struyf Knowledge Discovery in Inductive Databases: 5th International Workshop, KDID …, 2007 | 48 | 2007 |
| Finding explained groups of time-course gene expression profiles with predictive clustering trees I Slavkov, V Gjorgjioski, J Struyf, S Džeroski Molecular BioSystems 6 (4), 729-740, 2010 | 44 | 2010 |
| Using Decision Trees to Predict Forest Stand Height and Canopy Cover from LANDSAT and LIDAR Data. S Dzeroski, A Kobler, V Gjorgjioski, P Panov EnviroInfo, 125-133, 2006 | 22 | 2006 |
| Clustering analysis of vegetation data V Gjorgjioski, S Dzeroski, M White Jožef Stefan Inst., Ljubljana, Slovenia, Tech. Rep 10065, 2008 | 14 | 2008 |
| Comparison of distances for multi-label classification with PCTs V Gjorgjioski, D Kocev, S Džeroski Proc. Slovenian Conference on Data Mining and Data Warehouses, 2017 | 9 | 2017 |
| Stochastic propositionalization of relational data using aggregates V Gjorgjioski, S Dzeroski Jožef Stefan Institute, Tech. Rep, 2008 | 5 | 2008 |
| A Parallel Implementation of the LAGRAMGE Approach to Process-Based Modeling V Gjorgjioski, L Todorovski, S Dzeroski International Conference on Machine Learning, 2007 | 1 | 2007 |
| Predictive Clustering of Multi-dimensional Time Series Applied to Forest Growing Stock Data for Different Tree Sizes V Gjorgjioski, D Kocev, A Bončina, S Džeroski, M Debeljak ICT Innovations 2017: Data-Driven Innovation. 9th International Conference …, 2017 | | 2017 |
| Clustering analysis of vegetation data-IJSkt. ijs. si/DragiKocev/PhD/resources/lib/exe/fetch. php?...· 2011. 3. 14.· Clustering analysis of vegetation data Valentin Gjorgjioski V Gjorgjioski, S Dzeroski, M White | | |
| Predicting Forest Stand Height and Canopy Cover from LANDSAT and LIDAR data using decision trees S Dzeroski, A Kobler, V Gjorgjioski, P Panov | | |
