What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Statistics
- Machine learning
Bao-Gang Hu focuses on Pattern recognition, Artificial intelligence, Outlier, Facial recognition system and Control theory.
His biological study focuses on Sparse approximation.
The K-SVD and Discriminative model research Bao-Gang Hu does as part of his general Artificial intelligence study is frequently linked to other disciplines of science, such as Dense graph and Monotonic function, therefore creating a link between diverse domains of science.
His research in Outlier focuses on subjects like Robustness, which are connected to Optimization problem, Mean squared error, Linear least squares and Support vector machine.
His Facial recognition system research includes elements of Correlation clustering, Cluster analysis, Constrained clustering, Hidden Markov model and Brown clustering.
His research on Control theory often connects related topics like Fuzzy logic.
His most cited work include:
- Maximum Correntropy Criterion for Robust Face Recognition (508 citations)
- Analysis of direct action fuzzy PID controller structures (281 citations)
- Robust Principal Component Analysis Based on Maximum Correntropy Criterion (225 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, Algorithm and Mathematical optimization.
His work carried out in the field of Artificial intelligence brings together such families of science as Data mining and Computer vision.
He has researched Pattern recognition in several fields, including Facial recognition system, Outlier and Cluster analysis.
His Outlier study combines topics from a wide range of disciplines, such as Sparse approximation and Robustness.
His studies in Machine learning integrate themes in fields like Normalization and Information theory.
Lagrange multiplier and Optimization problem are subfields of Mathematical optimization in which his conducts study.
He most often published in these fields:
- Artificial intelligence (51.64%)
- Pattern recognition (24.88%)
- Machine learning (20.66%)
What were the highlights of his more recent work (between 2015-2021)?
- Artificial intelligence (51.64%)
- Machine learning (20.66%)
- Pattern recognition (24.88%)
In recent papers he was focusing on the following fields of study:
The scientist’s investigation covers issues in Artificial intelligence, Machine learning, Pattern recognition, Artificial neural network and Benchmark.
His study in Conjugacy class extends to Artificial intelligence with its themes.
His study connects Facial recognition system and Machine learning.
The study incorporates disciplines such as Image segmentation, Outlier and Cluster analysis in addition to Pattern recognition.
His Artificial neural network study combines topics in areas such as Lagrange multiplier, Affective computing and Constraint.
His research integrates issues of Margin and Ranking in his study of Benchmark.
Between 2015 and 2021, his most popular works were:
- Robust support vector machines based on the rescaled hinge loss function (49 citations)
- LGM-Net: Learning to Generate Matching Networks for Few-Shot Learning (27 citations)
- Weakly-Supervised Deep Convolutional Neural Network Learning for Facial Action Unit Intensity Estimation (27 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Machine learning
- Statistics
His primary scientific interests are in Artificial intelligence, Pattern recognition, Machine learning, Benchmark and Face.
His Artificial intelligence study frequently draws connections to other fields, such as Estimator.
The concepts of his Pattern recognition study are interwoven with issues in Correlation clustering, Cluster analysis, Image segmentation and Outlier.
His work on Identifiability as part of general Machine learning research is frequently linked to Process, Symbolic computation and Information geometry, thereby connecting diverse disciplines of science.
The Benchmark study combines topics in areas such as Affective computing, Similarity, Convolutional neural network and Algorithm.
His research in Classifier intersects with topics in Facial recognition system, Hinge loss and Robustness.
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