This second edition of Adaptive Filters: Theory and Applications has been updated
throughout to reflect the latest developments in this field; notably an increased coverage …

Adaptive filtering algorithms fall into four main groups: recursive least squares (RLS)
algorithms and the corresponding fast versions; QR-and inverse QR-least squares …

An iterative block training method for support vector classifiers (SVCs) based on weighted
least squares (WLS) optimization is presented. The algorithm, which minimizes structural …

This paper introduces a new adaptive filtering algorithm called fast affine projections (FAP).
Its main attributes include RLS (recursive least squares) like convergence and tracking with …

We extend the discrete-time Chandrasekhar recursions for least-squares estimation in
constant parameter state-space models to a class of structured time-variant state-space …

The central problem in estimation is to recover, to good accuracy, a set of unobservable
parameters from corrupted data. Several optimization criteria have been used for estimation …

Blind image restoration is the process of estimating both the true image and the blur from the
degraded image, using only partial information about degradation sources and the imaging …

In the past years, active noise control (ANC) gained more and more popularity on the
consumer market. It suppresses environmental noise, whilst preserving the wearing comfort …

This brief proposes a new state-regularized (SR) and QR-decomposition-based (QRD)
recursive least squares (RLS) adaptive filtering algorithm with a variable forgetting factor …

This paper proposes a new variable regularized QR decompPosition (QRD)-based
recursive least M-estimate (VR-QRRLM) adaptive filter and studies its convergence …