Laboratory for Machine Learning and Computational Optimization
The Laboratory for Machine Learning and Computational Optimization is founded in July 2016. The laboratory is located in the School of Mathematical Sciences at the South China Normal University. The laboratory has the experts and scholars from home and abroad. The research areas focus on the mathematical theories of machine learning including approximation theory, nonsmooth analysis, support vector machines, artificial neural networks, image registrations, and so on. The original research study is conducted in the artificial intelligence, for examples, sparse machine learning and generalized data analysis. The learning algorithms are further applied to the big data analysis of education and medicine to develop the educational softwares and the medical softwares.
Colloquiums and ConferencesMore
- 2021-11-16 Linearized Proximal Algorithms for Convex Composite Optimization with Applications
- 2021-11-16Quadratic Matrix Inequality Approach to Robust Adaptive Beamforming for General-Rank Signal Model
- 2016-11-03Scalable Gaussian Process Analysis and Nonparametric Learning
- 2016-09-29Global-Local-Integration-based Kernel Approximation Methods
- 2017-07-04Randomized Block Coordinate Descent Methods for A Class of Structured Nonlinear Optimization
- 2017-10-29Big Data through The Looking-Glass of Super-Resolution
NewsMore
- 2021-06-28Workshop on Non-smooth Optimization and Machine Learning
- 2021-06-02Medical image registration technology and Application XVIII
- 2021-05-26Medical image registration technology and Application XVII
- 2021-05-24Medical image registration technology and Application XVI
- 2021-05-24Medical image registration technology and Application XV
- 2021-05-24Medical image registration technology and Application XIV