Bernard Zenko

According to our database1, Bernard Zenko authored at least 30 papers between 2001 and 2021.

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



In proceedings 
PhD thesis 




FOCUSED-Short-Term Wind Speed Forecast Correction Algorithm Based on Successive NWP Forecasts for Use in Traffic Control Decision Support Systems.
Sensors, 2021

Improving Effectiveness of a Coaching System Through Preference Learning.
Proceedings of the PETRA '21: The 14th PErvasive Technologies Related to Assistive Environments Conference, Virtual Event, Greece, 29 June, 2021

Reconstructing dynamical networks via feature ranking.
CoRR, 2019

Social activity modelling and multimodal coaching for active aging.
Proceedings of the 12th ACM International Conference on PErvasive Technologies Related to Assistive Environments, 2019

Multi-criteria Modelling Approach for Ambient Assisted Coaching of Senior Adults.
Proceedings of the 11th International Joint Conference on Knowledge Discovery, 2019

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

MetaBags: Bagged Meta-Decision Trees for Regression.
Proceedings of the Machine Learning and Knowledge Discovery in Databases, 2018

Decoupling approximation robustly reconstructs directed dynamical networks.
CoRR, 2017

A study of overfitting in optimization of a manufacturing quality control procedure.
Appl. Soft Comput., 2017

Clusters of male and female Alzheimer's disease patients in the Alzheimer's Disease Neuroimaging Initiative (ADNI) database.
Brain Informatics, 2016

Multilayer Clustering: Biomarker Driven Segmentation of Alzheimer's Disease Patient Population.
Proceedings of the Bioinformatics and Biomedical Engineering, 2015

Text Detection in Document Images by Machine Learning Algorithms.
Proceedings of the 9th International Conference on Computer Recognition Systems CORES 2015, 2015

Identification of Gender Specific Biomarkers for Alzheimer's Disease.
Proceedings of the Brain Informatics and Health - 8th International Conference, 2015

Multi-target regression with rule ensembles.
J. Mach. Learn. Res., 2012

Estimating the risk of fire outbreaks in the natural environment.
Data Min. Knowl. Discov., 2012

Speeding-Up Hoeffding-Based Regression Trees With Options.
Proceedings of the 28th International Conference on Machine Learning, 2011

Predictive models of forest development in Slovenia.
Proceedings of the Innovations in Sharing Environmental Observations and Information: Proceedings of the 25th International Conference on Informatics for Environmental Protection, 2011

Evaluation Method for Feature Rankings and their Aggregations for Biomarker Discovery.
Proceedings of the third International Workshop on Machine Learning in Systems Biology, 2010

Machine Learning, Ensemble Methods in.
Proceedings of the Encyclopedia of Complexity and Systems Science, 2009

Rule Ensembles for Multi-target Regression.
Proceedings of the ICDM 2009, 2009

Learning Predictive Clustering Rules.
Informatica (Slovenia), 2008

Learning Classification Rules for Multiple Target Attributes.
Proceedings of the Advances in Knowledge Discovery and Data Mining, 2008

Learning Predictive Clustering Rules.
Proceedings of the Knowledge Discovery in Inductive Databases, 4th International Workshop, 2005

A report on the fourth international workshop on environmental applications of machine learning (EAML 2004).
SIGKDD Explor., 2004

Is Combining Classifiers with Stacking Better than Selecting the Best One?
Mach. Learn., 2004

Relating personality traits and mercury exposure in miners with machine learning methods.
Proceedings of the Sh@ring: 18th International Conference "Informatics for Environmental Protection", 2004

Stacking with Multi-response Model Trees.
Proceedings of the Multiple Classifier Systems, Third International Workshop, 2002

Is Combining Classifiers Better than Selecting the Best One.
Proceedings of the Machine Learning, 2002

Stacking with an Extended Set of Meta-level Attributes and MLR.
Proceedings of the Machine Learning: ECML 2002, 2002

A Comparison of Stacking with Meta Decision Trees to Bagging, Boosting, and Stacking with other Methods.
Proceedings of the 2001 IEEE International Conference on Data Mining, 29 November, 2001