What is she best known for?
The fields of study she is best known for:
- Artificial intelligence
- Machine learning
- Programming language
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 most cited work include:
- The multi-purpose incremental learning system AQ15 and its testing application to three medical domains (703 citations)
- Inductive Logic Programming: Techniques and Applications (677 citations)
- Relational Data Mining (523 citations)
What are the main themes of her work throughout her whole career to date?
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.
She most often published in these fields:
- Artificial intelligence (46.96%)
- Data mining (26.67%)
- Machine learning (28.12%)
What were the highlights of her more recent work (between 2017-2021)?
- Artificial intelligence (46.96%)
- Machine learning (28.12%)
- Natural language processing (11.59%)
In recent papers she was focusing on the following fields of study:
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.
Between 2017 and 2021, her most popular works were:
- HINMINE: heterogeneous information network mining with information retrieval heuristics (20 citations)
- Computational Creativity Infrastructure for Online Software Composition: A Conceptual Blending Use Case (19 citations)
- Py3plex: A Library for Scalable Multilayer Network Analysis and Visualization (14 citations)
In her most recent research, the most cited papers focused on:
- Artificial intelligence
- Machine learning
- Programming language
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.
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