What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Statistics
- Machine learning
Artificial intelligence, Pattern recognition, Algorithm, Artificial neural network and Model selection are his primary areas of study.
As part of his studies on Artificial intelligence, Lei Xu often connects relevant subjects like Machine learning.
His research integrates issues of Classifier and Handwriting recognition in his study of Machine learning.
His Pattern recognition research is multidisciplinary, relying on both Pixel and Randomized Hough transform, Hough transform.
His Algorithm study incorporates themes from Parameter space, Independent component analysis, Mathematical optimization, Series and Joint.
The concepts of his Artificial neural network study are interwoven with issues in Mean squared error, Principal component analysis and Robustness.
His most cited work include:
- Methods of combining multiple classifiers and their applications to handwriting recognition (2049 citations)
- A new curve detection method: randomized Hough transform (RHT) (873 citations)
- On convergence properties of the em algorithm for gaussian mixtures (633 citations)
What are the main themes of his work throughout his whole career to date?
His main research concerns Artificial intelligence, Pattern recognition, Machine learning, Model selection and Bayesian probability.
His research related to Artificial neural network, Unsupervised learning, Competitive learning, Cluster analysis and Semi-supervised learning might be considered part of Artificial intelligence.
Lei Xu has researched Artificial neural network in several fields, including Algorithm, Principal component analysis and Subspace topology.
The various areas that Lei Xu examines in his Algorithm study include Independent component analysis and Expectation–maximization algorithm.
His research on Pattern recognition often connects related areas such as Prior probability.
His Model selection research integrates issues from Minimum description length, Regularization, Bayesian information criterion and Akaike information criterion.
He most often published in these fields:
- Artificial intelligence (41.38%)
- Pattern recognition (19.47%)
- Machine learning (19.47%)
What were the highlights of his more recent work (between 2017-2021)?
- Chemical engineering (8.52%)
- Photocatalysis (2.64%)
- Artificial intelligence (41.38%)
In recent papers he was focusing on the following fields of study:
Lei Xu focuses on Chemical engineering, Photocatalysis, Artificial intelligence, Composite material and Anode.
His study looks at the relationship between Chemical engineering and fields such as Composite number, as well as how they intersect with chemical problems.
His work is dedicated to discovering how Photocatalysis, Heterojunction are connected with Selectivity and other disciplines.
Lei Xu combines subjects such as Machine learning and Pattern recognition with his study of Artificial intelligence.
Lei Xu has included themes like Fourier transform infrared spectroscopy and Copper in his Composite material study.
His Microstructure research incorporates elements of Alloy and Scanning electron microscope.
Between 2017 and 2021, his most popular works were:
- Study on the oxidative stabilization of polyacrylonitrile fibers by microwave heating (32 citations)
- Direct epitaxial synthesis of magnetic Fe3O4@UiO-66 composite for efficient removal of arsenate from water (27 citations)
- Global drought trends under 1.5 and 2 °C warming (25 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Statistics
- Machine learning
His primary scientific interests are in Chemical engineering, Precipitation, Inorganic chemistry, Environmental chemistry and Anode.
His Chemical engineering research includes themes of Scanning electron microscope, Composite number, Photocatalysis and Mesoporous material.
His work carried out in the field of Precipitation brings together such families of science as Evapotranspiration, Remote sensing and Water content.
His Inorganic chemistry research includes elements of Membrane, Langmuir adsorption model, Sodium hydroxide and Electrostatic attraction.
His work on Membrane fouling is typically connected to Naproxen as part of general Membrane study, connecting several disciplines of science.
His study in Environmental chemistry is interdisciplinary in nature, drawing from both Detection limit, Soil pH and Ultrafiltration.
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