What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Statistics
- Machine learning
His primary scientific interests are in Electric power system, Mathematical optimization, Artificial intelligence, Decision tree and Machine learning.
His studies in Electric power system integrate themes in fields like Stability, Control theory, Electrical network, Control engineering and Scheme.
His work carried out in the field of Mathematical optimization brings together such families of science as Nonlinear programming and Power flow.
He has researched Artificial intelligence in several fields, including Data mining and Computer simulation.
The study incorporates disciplines such as Proteomics, Ensemble learning, Interpretability, Decision theory and Robustness in addition to Decision tree.
He interconnects Regression analysis, Kernel method and Bias–variance tradeoff in the investigation of issues within Supervised learning.
His most cited work include:
- Extremely randomized trees (2672 citations)
- Inferring Regulatory Networks from Expression Data Using Tree-Based Methods (770 citations)
- Tree-Based Batch Mode Reinforcement Learning (719 citations)
What are the main themes of his work throughout his whole career to date?
Louis Wehenkel focuses on Artificial intelligence, Electric power system, Machine learning, Mathematical optimization and Decision tree.
His biological study focuses on Supervised learning.
His work deals with themes such as Stability, Control theory, Reliability engineering, Control engineering and Control, which intersect with Electric power system.
His study in Machine learning is interdisciplinary in nature, drawing from both Variable and Tree based.
His research in Mathematical optimization intersects with topics in Algorithm, AC power, Nonlinear programming and Reinforcement learning.
He has included themes like Interpretability and Robustness in his Decision tree study.
He most often published in these fields:
- Artificial intelligence (34.71%)
- Electric power system (32.23%)
- Machine learning (23.42%)
What were the highlights of his more recent work (between 2012-2021)?
- Electric power system (32.23%)
- Artificial intelligence (34.71%)
- Machine learning (23.42%)
In recent papers he was focusing on the following fields of study:
His scientific interests lie mostly in Electric power system, Artificial intelligence, Machine learning, Mathematical optimization and Probabilistic logic.
The concepts of his Electric power system study are interwoven with issues in Reliability, Control theory, Security management, Control and Smart grid.
His Artificial intelligence study integrates concerns from other disciplines, such as Tree and Pattern recognition.
His research integrates issues of Data mining, Branch and bound, Inference, Benchmark and Reliability in his study of Machine learning.
The various areas that Louis Wehenkel examines in his Mathematical optimization study include AC power, Nonlinear programming, Nonlinear system and Power system simulation.
His work in Supervised learning is not limited to one particular discipline; it also encompasses Decision tree.
Between 2012 and 2021, his most popular works were:
- Understanding variable importances in forests of randomized trees (394 citations)
- Contingency Ranking With Respect to Overloads in Very Large Power Systems Taking Into Account Uncertainty, Preventive, and Corrective Actions (154 citations)
- Collaborative analysis of multi-gigapixel imaging data using Cytomine (73 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Statistics
- Machine learning
The scientist’s investigation covers issues in Artificial intelligence, Electric power system, Machine learning, Mathematical optimization and Ensemble learning.
His Artificial intelligence study combines topics from a wide range of disciplines, such as Function, Batch processing and Pattern recognition.
His study in Electric power system is interdisciplinary in nature, drawing from both Service, Reliability engineering, Energy, Linear programming and Reliability.
His Machine learning research incorporates elements of Contextual image classification, Ranging, Segmentation and Automatic image annotation.
His work in Mathematical optimization addresses subjects such as Nonlinear system, which are connected to disciplines such as AC power.
His work deals with themes such as Tree and Random forest, which intersect with Ensemble learning.
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