What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Machine learning
- Programming language
Artificial intelligence, Machine learning, Multi-label classification, Data mining and Classifier are his primary areas of study.
His work carried out in the field of Artificial intelligence brings together such families of science as Field and Pattern recognition.
His Pruning, Ensemble learning and Support vector machine study in the realm of Machine learning connects with subjects such as Thresholding.
His work on Classifier chains as part of general Multi-label classification study is frequently connected to Disjoint sets, therefore bridging the gap between diverse disciplines of science and establishing a new relationship between them.
His Classifier chains research is multidisciplinary, incorporating elements of Theoretical computer science, Multi label learning, Single label, Latent semantic indexing and Web mining.
He combines subjects such as Supervised learning and Set with his study of Data mining.
His most cited work include:
- Mining Multi-label Data (1036 citations)
- Random k-Labelsets: An Ensemble Method for Multilabel Classification (594 citations)
- MULAN: A Java Library for Multi-Label Learning (524 citations)
What are the main themes of his work throughout his whole career to date?
Ioannis Vlahavas mainly focuses on Artificial intelligence, Machine learning, Data mining, Information retrieval and Task.
The concepts of his Artificial intelligence study are interwoven with issues in Domain and Pattern recognition.
His studies deal with areas such as Statistical classification, Translation initiation sites, Feature selection and Cluster analysis as well as Data mining.
His Information retrieval study incorporates themes from Web service and Metadata.
His work in Metadata addresses subjects such as RDF, which are connected to disciplines such as Knowledge base.
He has included themes like Set and Regression in his Random forest study.
He most often published in these fields:
- Artificial intelligence (35.31%)
- Machine learning (23.08%)
- Data mining (14.69%)
What were the highlights of his more recent work (between 2015-2021)?
- Artificial intelligence (35.31%)
- Task (8.74%)
- Machine learning (23.08%)
In recent papers he was focusing on the following fields of study:
Ioannis Vlahavas mainly investigates Artificial intelligence, Task, Machine learning, Information retrieval and Information and Computer Science.
His Artificial intelligence research includes themes of Field and Natural language processing.
His Task study integrates concerns from other disciplines, such as Identification, Deep learning, Multi-label classification, Heuristics and Search engine indexing.
In his study, Data mining, Support vector machine and Association rule learning is strongly linked to Supervised learning, which falls under the umbrella field of Multi-label classification.
Ioannis Vlahavas interconnects Operator and Process in the investigation of issues within Machine learning.
The various areas that Ioannis Vlahavas examines in his Information retrieval study include Classifier and Convolutional neural network.
Between 2015 and 2021, his most popular works were:
- Machine Learning and Data Mining Methods in Diabetes Research. (328 citations)
- Multi-target regression via input space expansion: treating targets as inputs (147 citations)
- Learning to Teach Reinforcement Learning Agents (29 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Programming language
- Machine learning
His main research concerns Information and Computer Science, Task, Supervised learning, Data mining and Ontology.
The concepts of his Task study are interwoven with issues in Question answering, Information retrieval, Search engine indexing and Multi-label classification.
He is investigating Machine learning and Artificial intelligence as part of his examination of Supervised learning.
Ioannis Vlahavas combines subjects such as Field and Data science with his study of Machine learning.
He undertakes interdisciplinary study in the fields of Artificial intelligence and Genetic data through his research.
His Data mining research is multidisciplinary, incorporating perspectives in Selection, Single-nucleotide polymorphism, Pairwise comparison and Feature selection.
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