What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Operating system
- Machine learning
Information retrieval, Data mining, Search engine, Ranking and Web page are his primary areas of study.
Nivio Ziviani regularly links together related areas like Ranking in his Information retrieval studies.
His research investigates the connection with Data mining and areas like Queries per second which intersect with concerns in Search-oriented architecture and Semantic search.
His Search engine research incorporates themes from Association rule learning and Parallel computing.
Newspaper and Key is closely connected to World Wide Web in his research, which is encompassed under the umbrella topic of Ranking.
His Web page research focuses on Similitude and how it relates to Directory, Classifier, Document classification and Document Structure Description.
His most cited work include:
- Fast and flexible word searching on compressed text (225 citations)
- Concept-based interactive query expansion (178 citations)
- Learning to advertise (152 citations)
What are the main themes of his work throughout his whole career to date?
His scientific interests lie mostly in Information retrieval, Data mining, Artificial intelligence, World Wide Web and Algorithm.
His research on Information retrieval often connects related topics like Web page.
His Data mining study combines topics from a wide range of disciplines, such as Ranking and Recommender system.
His Artificial intelligence research includes themes of Natural language processing, Machine learning and Pattern recognition.
His work carried out in the field of World Wide Web brings together such families of science as Key and Data science.
His study in the field of Time complexity also crosses realms of AVL tree, Hash table, Perfect hash function and Dynamic perfect hashing.
He most often published in these fields:
- Information retrieval (37.66%)
- Data mining (20.78%)
- Artificial intelligence (15.58%)
What were the highlights of his more recent work (between 2016-2020)?
- Artificial intelligence (15.58%)
- Information retrieval (37.66%)
- Machine learning (9.09%)
In recent papers he was focusing on the following fields of study:
Nivio Ziviani focuses on Artificial intelligence, Information retrieval, Machine learning, Pattern recognition and Deep learning.
His work deals with themes such as Tree, Software bug and Natural language processing, which intersect with Artificial intelligence.
His research integrates issues of Brier score and News aggregator in his study of Information retrieval.
As a part of the same scientific study, Nivio Ziviani usually deals with the Machine learning, concentrating on Software and frequently concerns with Encoding and Source code.
His Pattern recognition research includes themes of Interpretability and Perceptron.
His studies in Deep learning integrate themes in fields like Speech recognition, Key, Question answering, Discriminative model and Machine translation.
Between 2016 and 2020, his most popular works were:
- Learning Transferable Features for Speech Emotion Recognition (16 citations)
- Dynamic Prediction of ICU Mortality Risk Using Domain Adaptation (14 citations)
- Unsupervised and Scalable Algorithm for Learning Node Representations (13 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Operating system
- Machine learning
Nivio Ziviani spends much of his time researching Artificial intelligence, Deep learning, Node, Learning to rank and Context.
Nivio Ziviani has researched Artificial intelligence in several fields, including Data modeling and Key.
His research in Deep learning intersects with topics in Speech recognition, Discriminative model, Adaptation and Natural language processing.
The concepts of his Node study are interwoven with issues in Question answering, Representation, Feature learning and Scalable algorithms.
The research on Machine learning, Ranking and Ranking is part of his Learning to rank project.
Many of his Context research pursuits overlap with Theoretical computer science, Machine translation, Active listening, Space and Recommender system.
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