Quantized Kernel Least Mean Square Algorithm

By Badong Chen, Songlin Zhao, Pingping Zhu, José Carlos Príncipe. IEEE Transactions on Neural Networks and Learning Systems, 2012

400 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computational Mechanics, Computer Vision and Pattern Recognition

View Paper: https://www.semanticscholar.org/paper/23f4f3b72dc060b5989c0f8c183330cf02482dfa

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Cristobal Gallego-Castillo
2. Oscar Lopez-Garcia
3. Dacheng Tao
4. José Carlos Príncipe
5. Xinge You
6. Ricardo J. Bessa
7. Laura Cavalcante
8. Puskal P. Pokharel

In Computational Mechanics

1. Dacheng Tao
2. José Carlos Príncipe
3. Ricardo J. Bessa

In Computer Vision and Pattern Recognition

1. Dacheng Tao
2. José Carlos Príncipe
3. Xinge You
4. Luc Van Gool
5. Ming-Hsuan Yang
6. Changxing Ding
7. Xiaogang Wang
8. Ling Shao

Citations in the corpus

• Kernel Learning for Dynamic Texture Synthesis (2016, methodology): “…computational efficiency and save memory (a key issue for low-memory platforms), we substitute the standard state model by kernel adaptive filtering to develop DT synthesis using quantized kernel least mean squares (QKLMS), which we refer to as kernel adaptive dynamic texture synthesis (KADTS).”
• On-line quantile regression in the RKHS (Reproducing Kernel Hilbert Space) for operational probabilistic forecasting of wind power (2016, background): “Quantization of the feature space was presented as 13 an alternative in [36].”