What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Machine learning
- Computer vision
Dapeng Tao focuses on Artificial intelligence, Pattern recognition, Machine learning, Discriminative model and Regularization.
The Robustness, Principal component analysis and Support vector machine research Dapeng Tao does as part of his general Artificial intelligence study is frequently linked to other disciplines of science, such as Hessian matrix and Multi-task learning, therefore creating a link between diverse domains of science.
In the subject of general Pattern recognition, his work in Feature extraction and Canonical correlation is often linked to Rank and Component, thereby combining diverse domains of study.
His Machine learning study combines topics from a wide range of disciplines, such as Class, Covariance matrix and Hidden Markov model.
His study in Discriminative model is interdisciplinary in nature, drawing from both Image, Noise, Image fusion and Dimensionality reduction.
Dapeng Tao studied Regularization and Nonlinear dimensionality reduction that intersect with Annotation, Leverage, Logistic regression and Semi-supervised learning.
His most cited work include:
- Person Re-Identification by Regularized Smoothing KISS Metric Learning (143 citations)
- Hessian Regularized Support Vector Machines for Mobile Image Annotation on the Cloud (101 citations)
- Semantic preserving distance metric learning and applications (98 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, Computer vision and Discriminative model.
His study in Feature extraction, Regularization, Deep learning, Feature and Support vector machine is carried out as part of his studies in Artificial intelligence.
Dapeng Tao interconnects Feature, Overfitting, Data mining, Stability and Data set in the investigation of issues within Feature extraction.
His Pattern recognition study deals with Robustness intersecting with Outlier.
His work in the fields of Machine learning, such as Semi-supervised learning, overlaps with other areas such as Metric.
Dapeng Tao works mostly in the field of Discriminative model, limiting it down to concerns involving Principal component analysis and, occasionally, Speech recognition.
He most often published in these fields:
- Artificial intelligence (93.08%)
- Pattern recognition (59.75%)
- Machine learning (29.56%)
What were the highlights of his more recent work (between 2019-2021)?
- Artificial intelligence (93.08%)
- Pattern recognition (59.75%)
- Feature extraction (19.50%)
In recent papers he was focusing on the following fields of study:
Artificial intelligence, Pattern recognition, Feature extraction, Machine learning and Artificial neural network are his primary areas of study.
Many of his studies involve connections with topics such as Natural language processing and Artificial intelligence.
The study incorporates disciplines such as Contextual image classification, Regularization and Noise reduction in addition to Pattern recognition.
His studies in Feature extraction integrate themes in fields like Iterative method, Binary image, Iterative reconstruction and Dimensionality reduction.
Dapeng Tao combines subjects such as RGB color model and Cross modality with his study of Machine learning.
His Artificial neural network research incorporates themes from Representation and Hidden Markov model.
Between 2019 and 2021, his most popular works were:
- Discriminative Dictionary Learning-Based Multiple Component Decomposition for Detail-Preserving Noisy Image Fusion (18 citations)
- HesGCN: Hessian graph convolutional networks for semi-supervised classification (14 citations)
- Saliency Detection via a Multiple Self-Weighted Graph-Based Manifold Ranking (14 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Machine learning
- Computer vision
Dapeng Tao mostly deals with Artificial intelligence, Pattern recognition, Feature extraction, Machine learning and Graph.
His study in the field of Image fusion, Histogram and Noise reduction also crosses realms of Superposition principle and Noise measurement.
His study of Discriminative model is a part of Pattern recognition.
His Feature extraction research is multidisciplinary, incorporating elements of Perspective, Deep learning, Hidden Markov model and Feature vector.
In the field of Machine learning, his study on Re identification overlaps with subjects such as Invariant.
His work on Graph embedding as part of general Graph research is often related to Hessian matrix, thus linking different fields of science.
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