What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Machine learning
- The Internet
Alexander G. Hauptmann mostly deals with Artificial intelligence, Machine learning, Information retrieval, Pattern recognition and TRECVID.
His Artificial intelligence research is multidisciplinary, incorporating elements of Multimedia and Computer vision.
His studies in Multimedia integrate themes in fields like World Wide Web and Selection.
His Machine learning research includes themes of Classifier and Data mining.
His Information retrieval research incorporates elements of Context and Image retrieval, Relevance feedback.
He has researched Pattern recognition in several fields, including Precision and recall, Object detection, Feature and Boosting.
His most cited work include:
- Evaluating bag-of-visual-words representations in scene classification (675 citations)
- Large-scale concept ontology for multimedia (611 citations)
- Cross-domain video concept detection using adaptive svms (569 citations)
What are the main themes of his work throughout his whole career to date?
Artificial intelligence, Information retrieval, Machine learning, Multimedia and TRECVID are his primary areas of study.
His Artificial intelligence research incorporates themes from Natural language processing, Computer vision and Pattern recognition.
His Pattern recognition research is multidisciplinary, relying on both Contextual image classification and Image.
His Information retrieval study combines topics in areas such as Metadata, Visual Word, Image retrieval and Video tracking.
His work deals with themes such as Classifier, Representation and Training set, which intersect with Machine learning.
Alexander G. Hauptmann integrates TRECVID and Data mining in his studies.
He most often published in these fields:
- Artificial intelligence (54.79%)
- Information retrieval (22.61%)
- Machine learning (22.22%)
What were the highlights of his more recent work (between 2015-2021)?
- Artificial intelligence (54.79%)
- Machine learning (22.22%)
- Pattern recognition (16.86%)
In recent papers he was focusing on the following fields of study:
His primary areas of study are Artificial intelligence, Machine learning, Pattern recognition, Information retrieval and Multimedia.
His Artificial intelligence research is multidisciplinary, incorporating elements of Computer vision and Natural language processing.
The Convolutional neural network and Feature research Alexander G. Hauptmann does as part of his general Machine learning study is frequently linked to other disciplines of science, such as Trajectory and Modal, therefore creating a link between diverse domains of science.
In his research on the topic of Pattern recognition, Context is strongly related with Image.
He combines Information retrieval and TRECVID in his research.
He combines subjects such as Natural language and Feature with his study of Representation.
Between 2015 and 2021, his most popular works were:
- Person Re-identification: Past, Present and Future (562 citations)
- Bi-Level Semantic Representation Analysis for Multimedia Event Detection (196 citations)
- Feature Interaction Augmented Sparse Learning for Fast Kinect Motion Detection (150 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Machine learning
- The Internet
Alexander G. Hauptmann mainly investigates Artificial intelligence, Machine learning, Pattern recognition, Feature extraction and Information retrieval.
The Artificial intelligence study combines topics in areas such as Computer vision and Natural language processing.
His work on Feature as part of general Machine learning study is frequently linked to TRECVID and Path, therefore connecting diverse disciplines of science.
His Pattern recognition research integrates issues from Computational complexity theory, Outlier and Action.
His work in Feature extraction covers topics such as Artificial neural network which are related to areas like Pattern recognition.
The study incorporates disciplines such as Context, Multimedia and Metadata in addition to Information retrieval.
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