What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Machine learning
- Statistics
Ralf Herbrich mainly focuses on Artificial intelligence, Machine learning, Pattern recognition, Data mining and Support vector machine.
His Artificial intelligence course of study focuses on Metric and Data point, Interpretation and Margin.
Many of his research projects under Machine learning are closely connected to Data sampling with Data sampling, tying the diverse disciplines of science together.
His Data mining research is multidisciplinary, relying on both Recommender system, Metadata, Ordinal regression and Web service.
His Support vector machine research focuses on Discrete mathematics and how it connects with Kernel method.
His Mathematical analysis research incorporates elements of Applied mathematics, Kernel and Kernel principal component analysis.
His most cited work include:
- A Generalized Representer Theorem (1170 citations)
- A Generalized Representer Theorem (1170 citations)
- Large margin rank boundaries for ordinal regression (974 citations)
What are the main themes of his work throughout his whole career to date?
Artificial intelligence, Machine learning, Pattern recognition, Support vector machine and Data mining are his primary areas of study.
His Artificial intelligence study frequently intersects with other fields, such as Margin.
In Machine learning, Ralf Herbrich works on issues like Inference, which are connected to Graphical model.
Ralf Herbrich does research in Support vector machine, focusing on Kernel method specifically.
His Kernel method study improves the overall literature in Kernel.
His Relevance vector machine research incorporates themes from Sparse approximation and Structured support vector machine.
He most often published in these fields:
- Artificial intelligence (52.34%)
- Machine learning (28.91%)
- Pattern recognition (17.19%)
What were the highlights of his more recent work (between 2011-2020)?
- Artificial intelligence (52.34%)
- Machine learning (28.91%)
- Constraint (2.34%)
In recent papers he was focusing on the following fields of study:
Ralf Herbrich focuses on Artificial intelligence, Machine learning, Constraint, Social network and Bayesian probability.
His study in Artificial intelligence is interdisciplinary in nature, drawing from both Test and Pattern recognition.
His Machine learning study combines topics from a wide range of disciplines, such as Schema and Online advertising.
His Constraint research is multidisciplinary, incorporating elements of Computer vision and Robot manipulator.
As a part of the same scientific family, Ralf Herbrich mostly works in the field of Bayesian probability, focusing on Management science and, on occasion, Recommender system.
His Mixture model study incorporates themes from Latent Dirichlet allocation, Approximate inference, Variational message passing and Applied mathematics.
Between 2011 and 2020, his most popular works were:
- Practical Lessons from Predicting Clicks on Ads at Facebook (457 citations)
- Interactive interfaces for machine learning model evaluations (177 citations)
- Concurrent binning of machine learning data (44 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Machine learning
- Statistics
The scientist’s investigation covers issues in Artificial intelligence, Machine learning, Game of chance, Forecast skill and Human–computer interaction.
His research on Artificial intelligence often connects related topics like Social network.
His studies deal with areas such as Schema and Online advertising as well as Machine learning.
He combines subjects such as Multimedia and Game Developer with his study of Game of chance.
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