Distributed learning for Random Vector Functional-Link networks

By Simone Scardapane, Dianhui Wang, Massimo Panella, Aurelio Uncini. 2015

148 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computer Networks and Communications

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. Ponnuthurai Nagaratnam Suganthan
2. Simone Scardapane
3. Le Zhang
4. P. Lorenzo
5. Swagatam Das
6. Pinar Çivicioglu
7. Sankha Subhra Mullick
8. Yaochu Jin

In Computer Networks and Communications

1. Simone Scardapane
2. Yuhua Xu
3. Shuo Feng
4. Ponnuthurai Nagaratnam Suganthan
5. Guoru Ding
6. Le Zhang
7. P. Lorenzo
8. Angelia Nedić

Citations in the corpus

• A survey of machine learning for big data processing (2016, background): “For example, in [113], two distributed learning algorithms for training random”
• A Framework for Parallel and Distributed Training of Neural Networks (2016, background): “…(Mateos et al., 2010; Di Lorenzo and Sayed, 2013), kernel ridge regression (Predd et al., 2006, 2009), random-weights networks (Huang and Li, 2015; Scardapane et al., 2015, 2016c), support vector machines (Navia-Vázquez et al., 2006; Lu et al., 2008; Forero et al., 2010; Scardapane et al.,…”
• A comprehensive evaluation of random vector functional link networks (2016, background): “In [34], distributed learning of RVFL was proposed where training data is distributed under a decentralized information structure.”