What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Machine learning
- Statistics
His scientific interests lie mostly in Artificial intelligence, Pattern recognition, Support vector machine, Machine learning and Numerical analysis.
His Artificial intelligence study is mostly concerned with Variable kernel density estimation, Classifier, Contextual image classification, Image segmentation and Feature extraction.
His studies in Support vector machine integrate themes in fields like Categorization, Robustness and Dimensionality reduction.
The Instance-based learning research he does as part of his general Machine learning study is frequently linked to other disciplines of science, such as Multi-task learning, Interpretation and Statistical reasoning, therefore creating a link between diverse domains of science.
His Instance-based learning research includes elements of Feature vector, Instance selection, Supervised learning, Similarity measure and Similitude.
His Feature selection research is multidisciplinary, relying on both Linear predictor function, Proper linear model, Linear model and Ranking SVM.
His most cited work include:
- MILES: Multiple-Instance Learning via Embedded Instance Selection (615 citations)
- Dimensionality reduction via sparse support vector machines (398 citations)
- Active learning via transductive experimental design (263 citations)
What are the main themes of his work throughout his whole career to date?
Artificial intelligence, Pattern recognition, Machine learning, Data mining and Support vector machine are his primary areas of study.
His work in Artificial intelligence is not limited to one particular discipline; it also encompasses Computer vision.
He combines subjects such as Contextual image classification and Heart wall with his study of Pattern recognition.
In general Machine learning, his work in Statistical model is often linked to Multi-task learning linking many areas of study.
Jinbo Bi interconnects Probabilistic logic and Missing data, Imputation in the investigation of issues within Data mining.
His Support vector machine study often links to related topics such as Regression analysis.
He most often published in these fields:
- Artificial intelligence (45.88%)
- Pattern recognition (22.94%)
- Machine learning (21.76%)
What were the highlights of his more recent work (between 2018-2021)?
- Artificial intelligence (45.88%)
- Algorithm (8.24%)
- Deep learning (4.71%)
In recent papers he was focusing on the following fields of study:
Jinbo Bi spends much of his time researching Artificial intelligence, Algorithm, Deep learning, Theoretical computer science and Rate of convergence.
His Artificial intelligence study frequently draws connections to adjacent fields such as Machine learning.
Jinbo Bi has included themes like Convolution and Graph in his Algorithm study.
His Deep learning study combines topics from a wide range of disciplines, such as Segmentation, Pattern recognition and Mammography.
Many of his research projects under Pattern recognition are closely connected to Metric and Context with Metric and Context, tying the diverse disciplines of science together.
The Theoretical computer science study combines topics in areas such as Multivariate statistics, Parameterized complexity and Pairwise comparison.
Between 2018 and 2021, his most popular works were:
- Machine-Learning-Assisted De Novo Design of Organic Molecules and Polymers: Opportunities and Challenges. (24 citations)
- Alcohol Expectancy and Cerebral Responses to Cue-Elicited Craving in Adult Nondependent Drinkers (14 citations)
- Multi-view cluster analysis with incomplete data to understand treatment effects (10 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Machine learning
- Statistics
Jinbo Bi mostly deals with Data mining, Artificial intelligence, Deep learning, Rate of convergence and Scale.
Jinbo Bi has researched Data mining in several fields, including Calibration, Activation function and Missing data.
His research integrates issues of Analytics and Cluster analysis in his study of Missing data.
His work carried out in the field of Artificial intelligence brings together such families of science as Mammography and Pattern recognition.
His Mammography research integrates issues from Classifier and Convolutional neural network.
His work on Region growing is typically connected to Metric and Context as part of general Segmentation study, connecting several disciplines of science.
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