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.
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.
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.
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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MILES: Multiple-Instance Learning via Embedded Instance Selection
Yixin Chen;Jinbo Bi;J.Z. Wang.
IEEE Transactions on Pattern Analysis and Machine Intelligence (2006)
Dimensionality reduction via sparse support vector machines
Jinbo Bi;Kristin Bennett;Mark Embrechts;Curt Breneman.
Journal of Machine Learning Research (2003)
Active learning via transductive experimental design
Kai Yu;Jinbo Bi;Volker Tresp.
international conference on machine learning (2006)
Numerical Simulation of Crack Growth and Coalescence in Rock-Like Materials Containing Multiple Pre-existing Flaws
X. P. Zhou;J. Bi;Q. H. Qian.
Rock Mechanics and Rock Engineering (2015)
Regression error characteristic curves
Jinbo Bi;Kristin P. Bennett.
international conference on machine learning (2003)
Support Vector Classification with Input Data Uncertainty
Jinbo Bi;Tong Zhang.
neural information processing systems (2004)
Prediction of protein retention times in anion-exchange chromatography systems using support vector regression.
Minghu Song;Curt M. Breneman;Jinbo Bi;Nagamani Sukumar.
Journal of Chemical Information and Computer Sciences (2002)
Bayesian multiple instance learning: automatic feature selection and inductive transfer
Vikas C. Raykar;Balaji Krishnapuram;Jinbo Bi;Murat Dundar.
international conference on machine learning (2008)
End-to-end Structure-Aware Convolutional Networks for Knowledge Base Completion
Chao Shang;Yun Tang;Jing Huang;Jinbo Bi.
arXiv: Artificial Intelligence (2018)
A Geometric Approach to Support Vector Regression
Jinbo Bi;Kristin P. Bennett.
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