What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- The Internet
- Machine learning
Helmut Prendinger mainly focuses on Artificial intelligence, Natural language processing, Affect, Human–computer interaction and Multimedia.
His work carried out in the field of Artificial intelligence brings together such families of science as Machine learning, Task and Complement.
His Natural language processing research is multidisciplinary, incorporating perspectives in Rhetorical Structure Theory, Affect and Verb.
His Affect research includes elements of User interface, Expression, State and Avatar.
His Human–computer interaction study integrates concerns from other disciplines, such as Interface, Gesture and Embodied cognition.
His Multimedia research integrates issues from Java, Markup language, Scripting language, Prolog and Natural language.
His most cited work include:
- THE EMPATHIC COMPANION: A CHARACTER-BASED INTERFACE THAT ADDRESSES USERS' AFFECTIVE STATES (202 citations)
- HILDA: A Discourse Parser Using Support Vector Machine Classification (200 citations)
- Life-like characters : tools, affective functions, and applications (169 citations)
What are the main themes of his work throughout his whole career to date?
Helmut Prendinger mainly investigates Human–computer interaction, Artificial intelligence, Multimedia, Natural language processing and Affect.
Helmut Prendinger has researched Human–computer interaction in several fields, including User interface, Interface, Markup language and Scripting language.
Helmut Prendinger has included themes like Machine learning and Task in his Artificial intelligence study.
His study in Multimedia is interdisciplinary in nature, drawing from both Visualization, Virtual reality, Gaze and Presentation.
His work deals with themes such as Rhetorical Structure Theory, Principle of compositionality and Verb, which intersect with Natural language processing.
His studies deal with areas such as Affective computing, Cognitive psychology, Avatar and Job interview as well as Affect.
He most often published in these fields:
- Human–computer interaction (32.91%)
- Artificial intelligence (33.33%)
- Multimedia (25.32%)
What were the highlights of his more recent work (between 2014-2021)?
- Artificial intelligence (33.33%)
- Multimedia (25.32%)
- Deep learning (2.95%)
In recent papers he was focusing on the following fields of study:
Helmut Prendinger focuses on Artificial intelligence, Multimedia, Deep learning, Natural language processing and Field.
Helmut Prendinger interconnects Machine learning, Task and Representation in the investigation of issues within Artificial intelligence.
His biological study spans a wide range of topics, including Affordance, Virtual reality and Human–computer interaction.
Human–computer interaction and ICO are two areas of study in which Helmut Prendinger engages in interdisciplinary work.
His Natural language processing study combines topics in areas such as Ranking, Relevance, Rhetorical Structure Theory, Tree and Query expansion.
His Cognitive psychology research is multidisciplinary, relying on both Affect and Ambiguity.
Between 2014 and 2021, his most popular works were:
- Forecasting fault events for predictive maintenance using data-driven techniques and ARMA modeling (68 citations)
- Deep learning for affective computing: Text-based emotion recognition in decision support (62 citations)
- Okutama-Action: An Aerial View Video Dataset for Concurrent Human Action Detection (55 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- The Internet
- Machine learning
Artificial intelligence, Field, Machine learning, Sentiment analysis and Transfer of learning are his primary areas of study.
While the research belongs to areas of Machine learning, Helmut Prendinger spends his time largely on the problem of Task, intersecting his research to questions surrounding Segmentation, Model compression and Dropout.
Sentiment analysis is a primary field of his research addressed under Natural language processing.
His Transfer of learning research is multidisciplinary, incorporating elements of Affective computing, Recurrent neural network and Deep learning.
His Recurrent neural network study incorporates themes from Cognitive psychology, Decision support system, Affect and Ambiguity.
His research in Decision support system intersects with topics in Reinforcement learning, Industrial organization and Trading strategy.
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