Recurrent least squares support vector machines

By Johan A. K. Suykens, Joos Vandewalle. 2000

366 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Control and Systems Engineering, Statistical and Nonlinear Physics

View Paper: https://www.semanticscholar.org/paper/cf5a39d7ba0483683af2b010a13850d466f43229

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. J. Urgen Schmidhuber
2. Faustino J. Gomez
3. Daan Wierstra
4. Yoshua Bengio
5. Sergey Levine
6. Shimon Whiteson
7. Matteo Gagliolo
8. Bart Baesens

In Control and Systems Engineering

1. Bart Baesens
2. J. Urgen Schmidhuber
3. Faustino J. Gomez
4. Johan A. K. Suykens
5. Tony Van Gestel
6. B. De Moor
7. Mohammad Hossein Ahmadi
8. Lukas Lukas

In Statistical and Nonlinear Physics

1. J. Urgen Schmidhuber
2. Faustino J. Gomez
3. Daan Wierstra
4. Matteo Gagliolo
5. Jos De Brabanter
6. Lukas Lukas
7. M. Alhuyi Nazari
8. Roghayeh Ghasempour

Citations in the corpus

• Short-term prediction of groundwater level using improved random forest regression with a combination of random features (2018, background): “2002; Suykens and Vandewalle 2000).”
• Benchmarking Least Squares Support Vector Machine Classifiers (2004, methodology): “The primal-dual formulations of the SVM framework and the equality constraints of the LS-SVM formulation allow to make extensions towards recurrent neural networks (Suykens & Vandewalle, 2000) and nonlinear optimal control (Suykens, Vandewalle, & De Moor, 2001).”
• Weighted least squares support vector machines: robustness and sparse approximation (2002, methodology): “However, the use of equality constrained and SSE based formulations within LS-SVM greatly simpli es the formulations and allows us to extend the method to recurrent networks [31] and control applications [32].”
• An Introductory Study on Time Series Modeling and Forecasting (2013, background): “Furthermore a continuous time series can be easily transformed to a discrete one by merging data together over a specified time interval.”
• A proposed model to predict thermal conductivity ratio of Al2O3/EG nanofluid by applying least squares support vector machine (LSSVM) and genetic algorithm as a connectionist approach (2018, methodology): “The detailed concept of LSSVM and its procedure are represented in several studies [57, 61, 62].”
• Evolino for recurrent support vector machines (2005, methodology): “In a more sophisticated approach by Suykens and Vandewalle [13], a window of m previous output values is fed back as input to a recurrent model with a fixed kernel.”
• Financial time series prediction using least squares support vector machines within the evidence framework (2001, background): “tions of LS-SVMs have allowed to make extensions toward recurrent neural networks [28] and nonlinear optimal control [29].”