A Kernel Subspace Method by Stochastic Realization for Learning Nonlinear Dynamical Systems

By Yoshinobu Kawahara, Takehisa Yairi, K. Machida. 2006

20 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Control and Systems Engineering

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. Sergey Levine
2. Byron Boots
3. Pieter Abbeel
4. J. Bagnell
5. Dieter Fox
6. Chelsea Finn
7. Geoffrey J. Gordon
8. Jan Peters

In Control and Systems Engineering

1. Dieter Fox
2. Byron Boots
3. Geoffrey J. Gordon
4. J. Bagnell
5. Arthur Gretton
6. Sergey Levine
7. Jonathan Ko
8. Arun Venkatraman

Citations in the corpus

• Hilbert Space Embeddings of Predictive State Representations (2013, methodology): “For example, the K-LDS learning algorithm generated an unstable model and the stabilized LDS had poor predictive accuracy.”
• Improving Multi-Step Prediction of Learned Time Series Models (2015, methodology): “Motivated by prior work in kernelized modeling of dynamical systems (Ralaivola and D’Alche-Buc 2004; Chaudhry et al. 2009; Wingate and Singh 2006; Kawahara, Yairi, and Machida 2006), we use kernel regression, a nonparametric learning procedure that allows us to embed our data points into a high,…”
• Learning GP-BayesFilters via Gaussian process latent variable models (2009, methodology): “The system we wish to model is the small problem presented in Kawahara et al. (2007) and Verdult et al. (2004).”