Bayesian Kernel Shaping for Learning Control

NeurIPS 2008 Jo-Anne TingMrinal KalakrishnanSethu VijayakumarStefan Schaal

In kernel-based regression learning, optimizing each kernel individually is useful when the data density, curvature of regression surfaces (or decision boundaries) or magnitude of output noise (i.e., heteroscedasticity) varies spatially. Unfortunately, it presents a complex computational problem as the danger of overfitting is high and the individual optimization of every kernel in a learning system may be overly expensive due to the introduction of too many open learning parameters... (read more)

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