What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Statistics
- Machine learning
Bernhard Schölkopf mainly focuses on Artificial intelligence, Support vector machine, Kernel method, Machine learning and Pattern recognition.
The study incorporates disciplines such as Theoretical computer science and Computer vision in addition to Artificial intelligence.
His Support vector machine study incorporates themes from Algorithm, Regularization and Feature vector.
His Kernel method study necessitates a more in-depth grasp of Kernel.
The Kernel study combines topics in areas such as Discrete mathematics, Kernel and Applied mathematics.
His research integrates issues of Contextual image classification, Nonparametric statistics and Set in his study of Pattern recognition.
His most cited work include:
- A tutorial on support vector regression (7388 citations)
- Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond (7343 citations)
- Nonlinear component analysis as a kernel eigenvalue problem (6463 citations)
What are the main themes of his work throughout his whole career to date?
His primary scientific interests are in Artificial intelligence, Machine learning, Pattern recognition, Algorithm and Support vector machine.
His Artificial intelligence study frequently draws connections to other fields, such as Computer vision.
His research is interdisciplinary, bridging the disciplines of Feature vector and Support vector machine.
The subject of his Kernel method research is within the realm of Kernel.
His Kernel research is multidisciplinary, incorporating perspectives in Embedding, Kernel and Applied mathematics.
His research in Radial basis function kernel is mostly concerned with Tree kernel.
He most often published in these fields:
- Artificial intelligence (46.55%)
- Machine learning (19.14%)
- Pattern recognition (17.16%)
What were the highlights of his more recent work (between 2019-2021)?
- Artificial intelligence (46.55%)
- Machine learning (19.14%)
- Kernel (10.86%)
In recent papers he was focusing on the following fields of study:
The scientist’s investigation covers issues in Artificial intelligence, Machine learning, Kernel, Algorithm and Reinforcement learning.
His Artificial intelligence research is multidisciplinary, relying on both Inductive bias, Generalization and Set.
His study on Feature learning is often connected to Process as part of broader study in Machine learning.
His studies deal with areas such as Embedding, Sample, Applied mathematics and Kernel as well as Kernel.
His Kernel research includes elements of Robust optimization, Reproducing kernel Hilbert space and Kernel method.
He has researched Algorithm in several fields, including Inference and Generative model.
Between 2019 and 2021, his most popular works were:
- From Variational to Deterministic Autoencoders (66 citations)
- A Spatiotemporal Epidemic Model to Quantify the Effects of Contact Tracing, Testing, and Containment (34 citations)
- Disentangling Factors of Variations Using Few Labels (29 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Statistics
- Machine learning
His primary areas of study are Artificial intelligence, Machine learning, Empirical research, Variation and Counterfactual thinking.
Artificial intelligence is closely attributed to Set in his work.
His Machine learning research incorporates themes from Object, Inductive bias, Encoder and Modular design.
Bernhard Schölkopf studied Encoder and Autoencoder that intersect with Algorithm.
His research in Empirical research intersects with topics in Cognitive psychology, Path, Generalization and Argument.
His study in Counterfactual thinking is interdisciplinary in nature, drawing from both Classifier and Mathematical economics.
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