What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Machine learning
- Statistics
His main research concerns Artificial intelligence, Pattern recognition, Support vector machine, Feature selection and Machine learning.
His Artificial intelligence study combines topics from a wide range of disciplines, such as Neuroimaging, Computer vision and Mild cognitive impairment.
Daoqiang Zhang combines subjects such as Multi-task learning, Subspace topology and Face with his study of Pattern recognition.
Daoqiang Zhang has included themes like Contextual image classification and Modality in his Support vector machine study.
His research in Feature selection intersects with topics in Cognition, Multikernel, Text mining, Kernel and Graph kernel.
His work in the fields of Artificial neural network overlaps with other areas such as Network security.
His most cited work include:
- Robust image segmentation using FCM with spatial constraints based on new kernel-induced distance measure (848 citations)
- Fast and robust fuzzy c-means clustering algorithms incorporating local information for image segmentation (797 citations)
- Multimodal Classification of Alzheimer’s Disease and Mild Cognitive Impairment (792 citations)
What are the main themes of his work throughout his whole career to date?
His scientific interests lie mostly in Artificial intelligence, Pattern recognition, Machine learning, Neuroimaging and Feature selection.
The Artificial intelligence study combines topics in areas such as Functional magnetic resonance imaging and Cognitive impairment.
His Pattern recognition research includes elements of Voxel and Cluster analysis.
His study in the fields of Regularization, Linear regression and Transfer of learning under the domain of Machine learning overlaps with other disciplines such as Modal.
His Neuroimaging research includes themes of Alzheimer's disease, Disease, Single-nucleotide polymorphism and Identification.
The various areas that Daoqiang Zhang examines in his Feature selection study include Multi-task learning, Modality, Feature learning and Feature.
He most often published in these fields:
- Artificial intelligence (89.46%)
- Pattern recognition (60.38%)
- Machine learning (35.46%)
What were the highlights of his more recent work (between 2019-2021)?
- Artificial intelligence (89.46%)
- Pattern recognition (60.38%)
- Functional magnetic resonance imaging (11.82%)
In recent papers he was focusing on the following fields of study:
Daoqiang Zhang mainly focuses on Artificial intelligence, Pattern recognition, Functional magnetic resonance imaging, Neuroimaging and Artificial neural network.
His research ties Machine learning and Artificial intelligence together.
His Pattern recognition research is multidisciplinary, incorporating elements of Leverage and Identification.
His biological study spans a wide range of topics, including Schizophrenia and Sparse approximation.
His studies in Artificial neural network integrate themes in fields like Magnetic resonance imaging, Residual, Atrophy and Cognitive impairment.
The concepts of his Feature selection study are interwoven with issues in Feature and Support vector machine.
Between 2019 and 2021, his most popular works were:
- Adaptive Feature Selection Guided Deep Forest for COVID-19 Classification With Chest CT (26 citations)
- Identifying Autism Spectrum Disorder With Multi-Site fMRI via Low-Rank Domain Adaptation (24 citations)
- Spatial-Temporal Dependency Modeling and Network Hub Detection for Functional MRI Analysis via Convolutional-Recurrent Network (19 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Machine learning
- Statistics
Daoqiang Zhang mainly investigates Artificial intelligence, Pattern recognition, Feature selection, Neuroimaging and Discriminative model.
His Artificial intelligence study frequently intersects with other fields, such as Autism spectrum disorder.
His Pattern recognition research is multidisciplinary, incorporating perspectives in Artificial neural network, Functional magnetic resonance imaging and Robustness.
Feature selection is a subfield of Machine learning that Daoqiang Zhang studies.
His Machine learning study integrates concerns from other disciplines, such as Hypergraph, Pairwise comparison and Multi-task learning.
His Discriminative model research is multidisciplinary, relying on both Dependency and Dementia.
This overview was generated by a machine learning system which analysed the scientist’s
body of work. If you have any feedback, you can
contact us
here.
