What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Machine learning
- Statistics
Joachim M. Buhmann spends much of his time researching Artificial intelligence, Pattern recognition, Segmentation, Cluster analysis and Image segmentation.
His Artificial intelligence research is multidisciplinary, relying on both Machine learning and Computer vision.
The Pattern recognition study combines topics in areas such as Contextual image classification, Embedding and Pairwise comparison.
His work on Image texture as part of general Segmentation research is often related to Associative property, thus linking different fields of science.
His Cluster analysis research is multidisciplinary, incorporating perspectives in Data mining and Dimensionality reduction.
Joachim M. Buhmann has researched Image segmentation in several fields, including Pixel, Optimization problem, Histogram and Active appearance model.
His most cited work include:
- Distortion invariant object recognition in the dynamic link architecture (1690 citations)
- Highly multiplexed imaging of tumor tissues with subcellular resolution by mass cytometry (750 citations)
- The Balanced Accuracy and Its Posterior Distribution (603 citations)
What are the main themes of his work throughout his whole career to date?
Joachim M. Buhmann mainly focuses on Artificial intelligence, Pattern recognition, Cluster analysis, Segmentation and Computer vision.
While the research belongs to areas of Artificial intelligence, Joachim M. Buhmann spends his time largely on the problem of Machine learning, intersecting his research to questions surrounding Inference.
His Pattern recognition study integrates concerns from other disciplines, such as Contextual image classification, Histogram, Feature and Image processing.
In most of his Cluster analysis studies, his work intersects topics such as Data mining.
He studies Computer vision, namely Pixel.
His Correlation clustering research integrates issues from Clustering high-dimensional data and Fuzzy clustering.
He most often published in these fields:
- Artificial intelligence (58.31%)
- Pattern recognition (37.60%)
- Cluster analysis (19.62%)
What were the highlights of his more recent work (between 2014-2021)?
- Artificial intelligence (58.31%)
- Pattern recognition (37.60%)
- Segmentation (19.35%)
In recent papers he was focusing on the following fields of study:
Joachim M. Buhmann mostly deals with Artificial intelligence, Pattern recognition, Segmentation, Algorithm and Artificial neural network.
His Artificial intelligence study combines topics in areas such as Machine learning and Computer vision.
His biological study spans a wide range of topics, including Sampling, Entropy and Softmax function.
His studies in Pattern recognition integrate themes in fields like Supervised learning, Random forest, Feature and Feature.
His work carried out in the field of Algorithm brings together such families of science as Bayesian probability, Probabilistic logic, Gaussian process and Robustness.
In his research on the topic of Artificial neural network, Spinal surgery, Stenosis and Magnetic resonance imaging is strongly related with Surgical planning.
Between 2014 and 2021, his most popular works were:
- Crowdsourcing the creation of image segmentation algorithms for connectomics (212 citations)
- Glaucoma detection using entropy sampling and ensemble learning for automatic optic cup and disc segmentation. (164 citations)
- TI-POOLING: Transformation-Invariant Pooling for Feature Learning in Convolutional Neural Networks (148 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Statistics
- Machine learning
Joachim M. Buhmann mainly focuses on Artificial intelligence, Pattern recognition, Segmentation, Computer vision and Machine learning.
Joachim M. Buhmann undertakes multidisciplinary studies into Artificial intelligence and Connectomics in his work.
His studies deal with areas such as Contextual image classification and Feature as well as Pattern recognition.
The concepts of his Segmentation study are interwoven with issues in Multiplexing and Tissue sections.
Many of his research projects under Computer vision are closely connected to Sparse matrix with Sparse matrix, tying the diverse disciplines of science together.
His work deals with themes such as Classifier and Active learning, which intersect with Machine learning.
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