Support vector machine techniques for nonlinear equalization

By Daniel J. Sebald, James A. Bucklew. IEEE Transactions on Signal Processing, 2000

327 citations recorded by the source corpus.

Subfields: Analytical Chemistry, Artificial Intelligence, Signal Processing

View Paper: https://www.semanticscholar.org/paper/d592544922d2a8849c4ca14867797ab76d0f0953

How researchers see this paper

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

In Analytical Chemistry

1. Shie Mannor
2. R. Meir
3. Yaakov Engel

In Artificial Intelligence

1. Shie Mannor
2. Jiajia Liu
3. Nei Kato
4. Fengxiao Tang
5. Yuichi Kawamoto
6. Yaakov Engel
7. R. Meir
8. Csaba Szepesvari

In Signal Processing

1. Nei Kato
2. Shie Mannor
3. R. Meir
4. Fengxiao Tang
5. Yaakov Engel
6. Paul Honeine
7. Davis E. King

Citations in the corpus

• Kernel Methods and Machine Learning (2014, unknown): “For a few examples, see [29, 35, 74, 89, 97, 103, 109, 113, 173, 184, 188, 211, 245, 256, 272, 273, 318].”
• Future Intelligent and Secure Vehicular Network Toward 6G: Machine-Learning Approaches (2020, background): “Sebald and Bucklew [47] proposed a support vector machine (SVM)-based nonlinear equalization algorithm in the wireless communication where the temporal correlation exists in the collected intersymbol interference (ISI) data.”
• The kernel recursive least-squares algorithm (2004, methodology): “In the present context f ( x , y ) = ( y − f ( x )) 2 .”