IEEE TNN Special Issue: Online Learning in Kernel Methods
Online learning is one of the most powerful and commonly used techniques for training adaptive filters and has been used successfully in neural networks. The last decade has also witnessed a flurry of research efforts in Mercer kernel methods, such as the SVM and kernel regression, kernel principal component analysis etc. We invite original and unpublished research contributions in all areas relevant to online learning with kernels. The papers will present original work or review state-of-the-art approaches that summarize the recent advances in the following non-exhaustive list of topics:
- Online learning for kernel adaptive systems
- Kernelization of online learning techniques
- Optimization, growing and pruning techniques and kernel design for online kernel learning
- Information theoretic learning principles in kernel adaptive systems
- Multidimensional kernel adaptive systems (complex, quaternion, and multichannel)
- Convergence, steady-state and error bound analysis of online kernel algorithms
- New applications of online learning with kernels
