What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Statistics
- Machine learning
Johan A. K. Suykens mostly deals with Support vector machine, Artificial intelligence, Least squares support vector machine, Artificial neural network and Pattern recognition.
His study in Support vector machine is interdisciplinary in nature, drawing from both Algorithm, Least squares, Linear discriminant analysis and Mathematical optimization.
His Mathematical optimization research focuses on subjects like Linear system, which are linked to Applied mathematics.
Johan A. K. Suykens focuses mostly in the field of Artificial intelligence, narrowing it down to matters related to Machine learning and, in some cases, Statistics.
His work in Least squares support vector machine covers topics such as Bayesian inference which are related to areas like Econometrics.
His work carried out in the field of Artificial neural network brings together such families of science as Control theory, Nonlinear modelling, Control theory and Nonlinear system.
His most cited work include:
- Least Squares Support Vector Machine Classifiers (6712 citations)
- Least Squares Support Vector Machines (2730 citations)
- Weighted least squares support vector machines: robustness and sparse approximation (993 citations)
What are the main themes of his work throughout his whole career to date?
His primary scientific interests are in Artificial intelligence, Support vector machine, Artificial neural network, Pattern recognition and Mathematical optimization.
His research combines Machine learning and Artificial intelligence.
The various areas that Johan A. K. Suykens examines in his Support vector machine study include Algorithm, Least squares and Data mining.
His research investigates the connection between Artificial neural network and topics such as Nonlinear system that intersect with issues in System identification, Scroll, Attractor and Topology.
His Convex optimization research extends to Mathematical optimization, which is thematically connected.
His research links Relevance vector machine with Least squares support vector machine.
He most often published in these fields:
- Artificial intelligence (37.38%)
- Support vector machine (31.86%)
- Artificial neural network (20.69%)
What were the highlights of his more recent work (between 2016-2021)?
- Artificial intelligence (37.38%)
- Algorithm (17.24%)
- Support vector machine (31.86%)
In recent papers he was focusing on the following fields of study:
Johan A. K. Suykens mainly investigates Artificial intelligence, Algorithm, Support vector machine, Kernel and Kernel.
He interconnects Machine learning and Pattern recognition in the investigation of issues within Artificial intelligence.
His Algorithm research incorporates elements of Sampling, Matrix and Feature vector.
His Support vector machine research is multidisciplinary, incorporating perspectives in Theoretical computer science, System of linear equations, System identification, Regression analysis and Least squares.
His Kernel research integrates issues from Positive-definite matrix, Hilbert space and Applied mathematics.
His Artificial neural network study combines topics from a wide range of disciplines, such as Feature selection and Component.
Between 2016 and 2021, his most popular works were:
- Deep-learning neural-network architectures and methods: Using component-based models in building-design energy prediction (70 citations)
- Multi-View Kernel Spectral Clustering (36 citations)
- Automated structural health monitoring based on adaptive kernel spectral clustering (28 citations)
In his most recent research, the most cited papers focused on:
- Statistics
- Artificial intelligence
- Machine learning
Artificial intelligence, Algorithm, Support vector machine, Kernel method and Pattern recognition are his primary areas of study.
Johan A. K. Suykens has included themes like Machine learning, Time series and Tensor in his Artificial intelligence study.
Johan A. K. Suykens combines subjects such as Sampling, Matrix, Kernel and System identification with his study of Algorithm.
His Support vector machine research includes themes of Least squares and Nonlinear system.
His Pattern recognition study combines topics in areas such as CURE data clustering algorithm, Spectral clustering, Cluster analysis and Canopy clustering algorithm.
His research integrates issues of Polynomial kernel, Kernel embedding of distributions, Mathematical optimization and Radial basis function kernel in his study of Kernel principal component analysis.
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