P. Panov

发表

A large-scale evaluation of computational protein function prediction

Daniel W. A. Buchan, Thomas A. Hopf, Mark N. Wass, 2013, Nature Methods.

Predicting Forest Stand Height and Canopy Cover from LANDSAT and LIDAR data using decision trees

S. Džeroski, P. Panov, A. Kobler, 2006 .

Probabilistic Active Learning : Towards Combining Versatility , Optimality and Efficiency

D. Kocev, P. Panov, S. Deroski, 2014 .

M 5317 M Machine Learning, Ensemble Methods In

P. Panov, 2022 .

SUMO-Supermodeling by combining imperfect models Year 2 Report-Workpackage 7

P. Panov, L. Todorovski, J. Tanevski, 2012 .

A catalogue with semantic annotations makes multilabel datasets FAIR

S. Džeroski, D. Kocev, P. Panov, 2022, Scientific reports.

Machine-learning ready data on the thermal power consumption of the Mars Express Spacecraft

S. Džeroski, D. Kocev, P. Panov, 2022, Scientific data.

Machine learning for effective spacecraft operation: Operating INTEGRAL through dynamic radiation environments

S. Džeroski, D. Kocev, P. Panov, 2022, Advances in Space Research.

FAIRification of MLC data

S. Džeroski, D. Kocev, P. Panov, 2022, ArXiv.

Explainable Model-specific Algorithm Selection for Multi-Label Classification

S. Džeroski, D. Kocev, P. Panov, 2022, 2022 IEEE Symposium Series on Computational Intelligence (SSCI).

Discovery Science

Sašo Džeroski, Ljupco Todorovski, Dragi Kocev, 2014, Lecture Notes in Computer Science.

Multi-label classification via multi-target regression on data streams

Saso Dzeroski, Pance Panov, Aljaz Osojnik, 2015, Machine Learning.

Comparison of Tree-Based Methods for Multi-target Regression on Data Streams

Saso Dzeroski, Pance Panov, Aljaz Osojnik, 2015, NFMCP.

Wet-dry-wet drug screen leads to the synthesis of TS1, a novel compound reversing lung fibrosis through inhibition of myofibroblast differentiation

S. Džeroski, D. Kocev, P. Panov, 2021, Cell Death & Disease.

Itemset Support Queries Using Frequent Itemsets and Their Condensed Representations

Saso Dzeroski, Taneli Mielikäinen, Pance Panov, 2006, Discovery Science.

Phyletic Profiling with Cliques of Orthologs Is Enhanced by Signatures of Paralogy Relationships

Saso Dzeroski, Tomislav Smuc, Pance Panov, 2013, PLoS Comput. Biol..

Estimating vegetation height and canopy cover from remotely sensed data with machine learning

Saso Dzeroski, Daniela Stojanova, Pance Panov, 2010, Ecol. Informatics.

Special issue on discovery science

Saso Dzeroski, Dragi Kocev, Pance Panov, 2016, Machine Learning.

Inductive Databases and Constraint-Based Data Mining

Pan e Panov, P. Panov, 2010 .

Combining Bagging and Random Subspaces to Create Better Ensembles

Saso Dzeroski, Pance Panov, S. Džeroski, 2007, IDA.

Ontology of core data mining entities

S. Džeroski, L. Soldatova, P. Panov, 2014, Data Mining and Knowledge Discovery.

Machine Learning, Ensemble Methods in

Bernard Zenko, Saso Dzeroski, Pance Panov, 2009, Encyclopedia of Complexity and Systems Science.

OPTION: OPTImization Algorithm Benchmarking ONtology

Saso Dzeroski, Tome Eftimov, Carola Doerr, 2021, IEEE Transactions on Evolutionary Computation.

AiTLAS: Artificial Intelligence Toolbox for Earth Observation

Nikola Simidjievski, Ivan Kitanovski, Ivica Dimitrovski, 2022, Remote. Sens..

Using Decision Trees to Predict Forest Stand Height and Canopy Cover from LANDSAT and LIDAR Data

Saso Dzeroski, Pance Panov, Andrej Kobler, 2006, EnviroInfo.

Using Simulation Models and Data Mining to Study Co-Existence of GM/Non-GM Crops at Regional Level

Saso Dzeroski, Antoine Messéan, Aneta Ivanovska, 2006, EnviroInfo.

Multi-criteria Modelling Approach for Ambient Assisted Coaching of Senior Adults

Zlatko Matjacic, Marko Bohanec, Martin Znidarsic, 2019, KEOD.

Generic ontology of datatypes

Saso Dzeroski, Larisa N. Soldatova, Pance Panov, 2016, Inf. Sci..

Neurodegenerative Disease Data Ontology

Larisa N. Soldatova, Pance Panov, Ana Kostovska, 2019, DS.

Incremental predictive clustering trees for online semi-supervised multi-target regression

Saso Dzeroski, Pance Panov, Aljaz Osojnik, 2020, Machine Learning.

OntoDM: An Ontology of Data Mining

Saso Dzeroski, Larisa N. Soldatova, Pance Panov, 2008, 2008 IEEE International Conference on Data Mining Workshops.

Towards reusable process-based models of dynamical systems: A case study in the domain of aquatic ecosystems

Saso Dzeroski, Nikola Simidjievski, Pance Panov, 2019, 2019 42nd International Convention on Information and Communication Technology, Electronics and Microelectronics (MIPRO).

Semantic Annotation of Predictive Modelling Experiments

Sašo Džeroski, Panče Panov, Ilin Tolovski, 2020, DS.

Utilizing Hierarchies in Tree-Based Online Structured Output Prediction

Saso Dzeroski, Pance Panov, Aljaz Osojnik, 2019, DS.

Towards an Ontology of Data Mining Investigations

Saso Dzeroski, Larisa N. Soldatova, Pance Panov, 2009, Discovery Science.

Tree-based methods for online multi-target regression

Saso Dzeroski, Pance Panov, Aljaz Osojnik, 2018, Journal of Intelligent Information Systems.

Using Knowledge Graphs for Performance Prediction of Modular Optimization Algorithms

S. Džeroski, P. Panov, Carola Doerr, 2023, EvoApplications@EvoStar.

Semantic Description of Data Mining Datasets: An Ontology-Based Annotation Schema

Sašo Džeroski, Ana Kostovska, Panče Panov, 2020, DS.

Modeling dynamical systems with data stream mining

Saso Dzeroski, Pance Panov, Aljaz Osojnik, 2016, Comput. Sci. Inf. Syst..

Discovering outliers in the Mars Express thermal power consumption patterns pdf

Nikola Simidjievski, Dragi Kocev, Luke Lucas, 2021, 2021 IEEE 8th International Conference on Space Mission Challenges for Information Technology (SMC-IT).

ML-Schema: Exposing the Semantics of Machine Learning with Schemas and Ontologies pdf

Joaquin Vanschoren, Diego Esteves, Larisa N. Soldatova, 2018, ICML 2018.

Machine Learning, Ensemble Methods in

S. Džeroski, P. Panov, B. Ženko, 2009, Encyclopedia of Complexity and Systems Science.

Machine-learning ready data on the thermal power consumption of the Mars Express Spacecraft

S. Džeroski, D. Kocev, P. Panov, 2022, Scientific Data.