What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Machine learning
- Artificial neural network
His scientific interests lie mostly in Artificial intelligence, Self-organizing map, Artificial neural network, Content-addressable memory and Learning vector quantization.
His study on Nervous system network models is often connected to Set as part of broader study in Artificial intelligence.
In his papers, he integrates diverse fields, such as Self-organizing map and Generative topographic map.
His research integrates issues of Algorithm and Pattern recognition in his study of Artificial neural network.
His Pattern recognition research incorporates themes from Field, Neural gas and Data science.
His work in the fields of Content-addressable memory, such as Autoassociative memory and Holographic associative memory, overlaps with other areas such as Memory model.
His most cited work include:
- Self-Organizing Maps (10652 citations)
- Self-organized formation of topologically correct feature maps (6958 citations)
- Self-Organization and Associative Memory (6687 citations)
What are the main themes of his work throughout his whole career to date?
Teuvo Kohonen mainly investigates Artificial intelligence, Self-organizing map, Artificial neural network, Information retrieval and Pattern recognition.
His studies in Learning vector quantization and Pattern recognition are all subfields of Artificial intelligence research.
His Self-organizing map study improves the overall literature in Cluster analysis.
His research in Artificial neural network focuses on subjects like Algorithm, which are connected to Associative property.
His work carried out in the field of Information retrieval brings together such families of science as Space, Word and World Wide Web.
His Pattern recognition research incorporates themes from Subspace topology and Feature.
He most often published in these fields:
- Artificial intelligence (46.11%)
- Self-organizing map (40.12%)
- Artificial neural network (26.35%)
What were the highlights of his more recent work (between 2000-2019)?
- Self-organizing map (40.12%)
- Artificial intelligence (46.11%)
- Artificial neural network (26.35%)
In recent papers he was focusing on the following fields of study:
The scientist’s investigation covers issues in Self-organizing map, Artificial intelligence, Artificial neural network, Cluster analysis and Data mining.
His studies in Self-organizing map integrate themes in fields like Vector quantization, Finite set, Library science, Bibliography and Addendum.
The concepts of his Vector quantization study are interwoven with issues in Independent set and Curse of dimensionality.
Teuvo Kohonen has researched Artificial intelligence in several fields, including Natural language processing, Machine learning, Visual cortex and Pattern recognition.
His Artificial neural network research is multidisciplinary, relying on both Feature, Memorization, Self-organization, Sensory system and Exploratory data analysis.
Teuvo Kohonen works mostly in the field of Cluster analysis, limiting it down to topics relating to Metric and, in certain cases, Node.
Between 2000 and 2019, his most popular works were:
- Self-Organizing Maps (1593 citations)
- Essentials of the self-organizing map (636 citations)
- Bibliography of Self-Organizing Map SOM) Papers: 1998-2001 Addendum (306 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Machine learning
- Artificial neural network
Self-organizing map, Artificial neural network, Bibliography, Addendum and Information retrieval are his primary areas of study.
His Self-organizing map research incorporates elements of Exploratory data analysis, Data mining, Data science and Finite set.
Teuvo Kohonen has included themes like Open set and Theoretical computer science in his Data mining study.
His Artificial neural network research is included under the broader classification of Artificial intelligence.
Teuvo Kohonen interconnects Transfer function, Pointwise and Central nervous system in the investigation of issues within Artificial intelligence.
His Information retrieval study combines topics from a wide range of disciplines, such as Basis, Expression, Encyclopedia and Text mining.
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