What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Machine learning
- Statistics
John Shawe-Taylor focuses on Artificial intelligence, Machine learning, Support vector machine, Algorithm and Kernel.
His Artificial intelligence research is multidisciplinary, incorporating elements of Hyperplane and Pattern recognition.
His study focuses on the intersection of Machine learning and fields such as Generalization with connections in the field of Approximation theory.
His Support vector machine research incorporates themes from Artificial neural network and Decision support system.
In the subject of general Kernel, his work in String kernel is often linked to Analogy, thereby combining diverse domains of study.
His study in Statistical learning theory is interdisciplinary in nature, drawing from both Semi-supervised learning, Software, GRASP and Presentation.
His most cited work include:
- An Introduction to Support Vector Machines and Other Kernel-based Learning Methods (10918 citations)
- Kernel Methods for Pattern Analysis (5086 citations)
- An introduction to Support Vector Machines (3760 citations)
What are the main themes of his work throughout his whole career to date?
John Shawe-Taylor spends much of his time researching Artificial intelligence, Machine learning, Support vector machine, Algorithm and Kernel method.
His Artificial intelligence study frequently intersects with other fields, such as Pattern recognition.
John Shawe-Taylor works in the field of Machine learning, namely Multiple kernel learning.
His Support vector machine research is multidisciplinary, incorporating perspectives in Margin, Data mining and Model selection.
The study incorporates disciplines such as Function, Generalization and Perceptron in addition to Algorithm.
His Kernel study typically links adjacent topics like Kernel.
He most often published in these fields:
- Artificial intelligence (41.92%)
- Machine learning (22.74%)
- Support vector machine (18.65%)
What were the highlights of his more recent work (between 2014-2021)?
- Artificial intelligence (41.92%)
- Machine learning (22.74%)
- Algorithm (18.47%)
In recent papers he was focusing on the following fields of study:
The scientist’s investigation covers issues in Artificial intelligence, Machine learning, Algorithm, Recommender system and Generalization.
His Artificial intelligence study integrates concerns from other disciplines, such as Natural language processing and Pattern recognition.
His work investigates the relationship between Machine learning and topics such as Neuroimaging that intersect with problems in Multiple kernel learning and Interpretability.
His research in Algorithm intersects with topics in Norm, Feature, Similarity and Lasso.
His research integrates issues of Open educational resources, Data science and Set in his study of Recommender system.
His Support vector machine study combines topics in areas such as Linear programming, Data mining and Feature selection.
Between 2014 and 2021, his most popular works were:
- The Human Behaviour-Change Project: harnessing the power of artificial intelligence and machine learning for evidence synthesis and interpretation (130 citations)
- Empirical Risk Minimization Under Fairness Constraints (113 citations)
- Challenges in representation learning (94 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Machine learning
- Statistics
John Shawe-Taylor spends much of his time researching Artificial intelligence, Machine learning, Support vector machine, Kernel method and Mathematical optimization.
His Artificial intelligence research includes elements of Wine, Linear regression and Time series.
He has included themes like Voxel and Neuroimaging in his Machine learning study.
John Shawe-Taylor studies Support vector machine, namely Statistical learning theory.
His Kernel method study combines topics from a wide range of disciplines, such as Dynamic programming, Markov decision process, Kernel and Conditional expectation.
His Mathematical optimization research includes themes of Classifier, Linear model and Data pre-processing.
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