What is she best known for?
The fields of study she is best known for:
- Artificial intelligence
- Machine learning
- Programming language
Lise Getoor spends much of her time researching Artificial intelligence, Probabilistic logic, Machine learning, Data mining and Statistical relational learning.
Lise Getoor interconnects Natural language processing, Structure and Social network in the investigation of issues within Artificial intelligence.
Her study in Probabilistic logic is interdisciplinary in nature, drawing from both Probabilistic database, Graphical model, Relational database and Inference.
In her research, Graph theory, Network classification and Viral marketing is intimately related to Collective classification, which falls under the overarching field of Machine learning.
The various areas that Lise Getoor examines in her Data mining study include Domain, Graph, Information extraction, Information retrieval and Resolution.
Her work deals with themes such as Online machine learning, Unsupervised learning, Instance-based learning, Structured prediction and Relational model, which intersect with Statistical relational learning.
Her most cited work include:
- Collective Classification in Network Data (1279 citations)
- Introduction to statistical relational learning (1016 citations)
- Learning Probabilistic Relational Models (957 citations)
What are the main themes of her work throughout her whole career to date?
Lise Getoor mainly investigates Artificial intelligence, Probabilistic logic, Machine learning, Data mining and Inference.
Lise Getoor has researched Artificial intelligence in several fields, including Graph, Social network, Natural language processing, Relational model and Statistical relational learning.
She has included themes like Relational calculus, Data science and Logical conjunction in her Statistical relational learning study.
Her Probabilistic logic research is multidisciplinary, incorporating perspectives in Probabilistic database, Scalability, Theoretical computer science and Graph.
As a part of the same scientific family, Lise Getoor mostly works in the field of Data mining, focusing on Information retrieval and, on occasion, Cluster analysis.
Her study looks at the relationship between Inference and topics such as Graphical model, which overlap with Markov chain.
She most often published in these fields:
- Artificial intelligence (36.36%)
- Probabilistic logic (24.34%)
- Machine learning (24.34%)
What were the highlights of her more recent work (between 2015-2020)?
- Artificial intelligence (36.36%)
- Probabilistic logic (24.34%)
- Statistical relational learning (18.48%)
In recent papers she was focusing on the following fields of study:
Lise Getoor mainly focuses on Artificial intelligence, Probabilistic logic, Statistical relational learning, Inference and Machine learning.
Her Natural language processing research extends to the thematically linked field of Artificial intelligence.
Her Probabilistic logic study combines topics from a wide range of disciplines, such as Similarity, Theoretical computer science, Recommender system and Human–computer interaction.
The concepts of her Statistical relational learning study are interwoven with issues in Graphical model and Data science.
Her biological study spans a wide range of topics, including Structure, Structured prediction and Data mining.
Her studies in Data mining integrate themes in fields like Graph and Information retrieval.
Between 2015 and 2020, her most popular works were:
- Hinge-loss Markov random fields and probabilistic soft logic (117 citations)
- A probabilistic approach for collective similarity-based drug-drug interaction prediction. (46 citations)
- Personalized explanations for hybrid recommender systems (44 citations)
In her most recent research, the most cited papers focused on:
- Artificial intelligence
- Machine learning
- Programming language
Her primary areas of study are Probabilistic logic, Artificial intelligence, Inference, Theoretical computer science and Machine learning.
Her Probabilistic logic research includes elements of Graphical model, Web service, Human–computer interaction and Personalization.
Lise Getoor combines subjects such as Intersection and Software system with her study of Artificial intelligence.
Her Inference research integrates issues from Affect, Structured prediction, Mathematical optimization, Generalization and Statistical relational learning.
As part of the same scientific family, Lise Getoor usually focuses on Theoretical computer science, concentrating on Scalability and intersecting with Metadata.
Lise Getoor works mostly in the field of Machine learning, limiting it down to topics relating to Similarity and, in certain cases, Transitive relation, as a part of the same area of interest.
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