2023 - Research.com Computer Science in Slovenia Leader Award
2022 - Research.com Computer Science in Slovenia Leader Award
Her scientific interests lie mostly in Artificial intelligence, Machine learning, Inductive logic programming, Knowledge extraction and Data mining. Her Artificial intelligence research incorporates elements of Multi-task learning and Natural language processing. The Machine learning study combines topics in areas such as Terminology, Knowledge representation and reasoning, Task and Sensitivity.
Her Inductive logic programming study combines topics from a wide range of disciplines, such as Algorithm, Accuracy paradox, Inductive programming and Local variable. Nada Lavrač combines subjects such as Relational database, Risk groups, World Wide Web and Data science with her study of Knowledge extraction. Her Data mining study incorporates themes from Interpretability and Contrast set.
Her primary areas of investigation include Artificial intelligence, Data mining, Machine learning, Knowledge extraction and Inductive logic programming. Her biological study spans a wide range of topics, including Statistical relational learning, Task and Natural language processing. Her studies deal with areas such as Set, Contrast set and Heuristic as well as Data mining.
In general Machine learning, her work in Decision tree and Data pre-processing is often linked to Transformation linking many areas of study. Her Knowledge extraction research is multidisciplinary, incorporating perspectives in Ontology, Information retrieval, Context, Workflow and Data science. Her Inductive logic programming research includes elements of Relational database, Inductive programming and Logic programming.
Nada Lavrač mainly focuses on Artificial intelligence, Machine learning, Natural language processing, Embedding and Artificial neural network. Her study in Complex network extends to Artificial intelligence with its themes. Nada Lavrač works mostly in the field of Machine learning, limiting it down to topics relating to Statistical relational learning and, in certain cases, Table and Inductive logic programming.
Her Natural language processing study integrates concerns from other disciplines, such as Ontology and Semantics. Her Embedding research includes themes of Social network, Modularity, Cluster analysis, Interaction network and Silhouette. Her research investigates the connection between Artificial neural network and topics such as Robustness that intersect with issues in Semantic space.
Nada Lavrač mainly investigates Artificial intelligence, Network analysis, Machine learning, Information retrieval and Complex network. Her study in the field of Artificial neural network also crosses realms of Data transformation. Her research in Machine learning intersects with topics in Data type, External Data Representation and Social network.
The study incorporates disciplines such as Context, Contextualization, Margin, Feature learning and Transfer of learning in addition to Information retrieval. The various areas that Nada Lavrač examines in her Complex network study include Python, Visualization, Scalability and Distributed computing. Her research in Natural language processing intersects with topics in Parallel algorithm and Robustness.
This overview was generated by a machine learning system which analysed the scientist’s body of work. If you have any feedback, you can contact us here.
The multi-purpose incremental learning system AQ15 and its testing application to three medical domains
Ryszard S. Michalski;Igor Mozetic;Jiarong Hong;Nada Lavrac.
national conference on artificial intelligence (1986)
Inductive Logic Programming: Techniques and Applications
Nada Lavrac;Saso Dzeroski.
Relational Data Mining
Saso Dzeroski;Nada Lavrac.
Rule Evaluation Measures: A Unifying View
Nada Lavrac;Peter A. Flach;Blaz Zupan.
inductive logic programming (1999)
Subgroup Discovery with CN2-SD
Nada Lavrač;Branko Kavšek;Peter Flach;Ljupčo Todorovski.
Journal of Machine Learning Research (2004)
Selected Techniques for Data Mining in Medicine
Artificial Intelligence in Medicine (1999)
Supervised Descriptive Rule Discovery: A Unifying Survey of Contrast Set, Emerging Pattern and Subgroup Mining
Petra Kralj Novak;Nada Lavrač;Geoffrey I. Webb.
Journal of Machine Learning Research (2009)
Propositionalization approaches to relational data mining
Stefan Kramer;Nada Lavrač;Peter Flach.
Relational Data Mining (2001)
Foundations of Rule Learning
Johannes Frnkranz;Dragan Gamberger;Nada Lavrac.
Learning nonrecursive definitions of relations with LINUS
Nada Lavrač;Sašo Džeroski;Marko Grobelnik.
EWSL'91 Proceedings of the 5th European Conference on European Working Session on Learning (1991)
If you think any of the details on this page are incorrect, let us know.
We appreciate your kind effort to assist us to improve this page, it would be helpful providing us with as much detail as possible in the text box below: