2019 - BBVA Foundation Frontiers of Knowledge Award
2016 - German National Academy of Sciences Leopoldina - Deutsche Akademie der Naturforscher Leopoldina – Nationale Akademie der Wissenschaften Informatics
2011 - Max Planck Research Award Intelligent systems
2006 - IAPR J. K. Aggarwal Prize "For advancing the field of kernel methods and showing its wide applicability to pattern recognition problems."
Member of the European Academy of Sciences and Arts
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 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.
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.
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.
This overview was generated by a machine learning system which analysed the scientist’s body of work. If you have any feedback, you can contact us here.
Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond
Bernhard Scholkopf;Alexander J. Smola.
Journal of the American Statistical Association (2001)
A tutorial on support vector regression
Alex J. Smola;Bernhard Schölkopf.
Statistics and Computing (2004)
Learning with kernels
Nonlinear component analysis as a kernel eigenvalue problem
Bernhard Schölkopf;Alexander Smola;Klaus-Robert Müller.
Neural Computation (1998)
Advances in kernel methods: support vector learning
Bernhard Schölkopf;Christopher J. C. Burges;Alexander J. Smola.
international conference on neural information processing (1999)
Semi-Supervised Learning (Chapelle, O. et al., Eds.; 2006) [Book reviews]
O. Chapelle;B. Scholkopf;A. Zien.
IEEE Transactions on Neural Networks (2009)
Estimating the Support of a High-Dimensional Distribution
Bernhard Schölkopf;John C. Platt;John C. Shawe-Taylor;Alex J. Smola.
Neural Computation (2001)
An introduction to kernel-based learning algorithms
K.-R. Muller;S. Mika;G. Ratsch;K. Tsuda.
IEEE Transactions on Neural Networks (2001)
Learning with Local and Global Consistency
Dengyong Zhou;Olivier Bousquet;Thomas N. Lal;Jason Weston.
neural information processing systems (2003)
Fisher discriminant analysis with kernels
S. Mika;G. Ratsch;J. Weston;B. Scholkopf.
ieee workshop on neural networks for signal processing (1999)
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