3 accepted papers
We previously proposed a joint learning scheme of Gaussian parameters (scales and centers) and coefficients for online nonlinear estimation. The instantaneous squared error cost in terms of the Gaussian scales, however, tends to have shallow slopes when the initial guess is far from optimal, causing…
We propose an efficient adaptive update method for the kernel parameters: the kernel coefficients, scales and centers. The mirror descent and the steepest descent method for squared error cost function are employed to update the kernel scales and centers, respectively. Although the problem considere…
This paper presents a stochastic behavior analysis of a kernel-based stochastic restricted-gradient descent method. The restricted gradient gives a steepest ascent direction within the so-called dictionary subspace. The analysis provides the transient and steady state performance in the mean squared…