Kernel Adaptive Filtering: A Comprehensive Introduction

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High-dimensional geometry of population responses in visual cortex (2018, unknown): “The projection of f onto eigenfunctions with smaller eigenvalues will be added to the system’s output only after more extensive training, at a speed proportional to the eigenvalues [16, 17, 18].”
Kernel Learning for Dynamic Texture Synthesis (2016, result): “that are sufficiently different) the model as new centers [42].”
Kernel Methods and Machine Learning (2014, unknown): “For example, see [62, 157].”
Estimation of the forgetting factor in kernel recursive least squares (2012, background): “The standard RLS formulation often includes a forgetting factor λ ∈ (0, 1] that allows it to deal with non-stationary systems to some extent, while in order to obtain true tracking capabilities a more general extended recursive least-squares (EX-RLS) algorithm is required [1, 2].”
Quantized Kernel Least Mean Square Algorithm (2012, background): “As the training data are sequentially (one by one) presented to the learning system, OKL requires much less memory and computational cost.”