First, the kernel Kalman rule (KKR) is presented as an approximate alternative to the KBR that estimates the embedding of the state based on a recursive least squares objective. Based on the KKR we present the kernel Kalman filter (KKF) that updates an embedding of the belief state and ...
least squares (redirected fromRecursive least squares algorithm) Thesaurus Medical least squares pl.n.Statistics A method of determining the curve that best describes the relationship between expected and observed sets of data by minimizing the sums of the squares of deviation between observed and expec...
Due to its fast convergence rate, the recursive least-squares (RLS) algorithm is very popular in many applications of adaptive filtering, including system identification scenarios. However, the computational complexity of this algorithm represents a major limitation in applications that involve long filter...
Mahoney, On the nystrom method for approximating a Gram matrix for improved kernel-based learning. J. Mach. Learn. Res. 6, 2153–2175 (2005) MathSciNet MATH Google Scholar Y. Engel, S. Mannor, R. Meir, The kernel recursive least-squares algorithm. IEEE Trans. Signal Process. 52(8),...
"A recursive least squares approach to the on-line adaptive control problem", Int. j. Control, Vol. 16, pp. 243-260, 1972.D. J. Sandoz and B. H. Swanick, "A recursive least squares approach to the on-line adaptive control problem", Int. j. Control, vol. 16,pp.243*260, 1972...
The concept of underdetermined recursive least-squares (URLS) adaptive filtering is introduced. In particular, the URLS algorithm is derived and shown to be a direct consequence of the principle of minimal disturbance. By exploiting the Hankel structure of the filter input matrix, the fast transversa...
Sketch RLS is an adaptive filtering algorithm that brings sketching ideas into the classical recursive least squares algorithm. This is the python implementation of the algorithm. - LCAV/sketchrls
Triplet loss is a type of learning algorithm used in machine learning. It is called "triplet loss" because it uses three things to help a computer learn: a positive example, a negative example, and a "anchor" point. Imagine you have a computer that is trying to learn what a cat looks...
In this paper, we study the parameter estimation problem of a class of output nonlinear systems and propose a recursive least squares (RLS) algorithm for estimating the parameters of the nonlinear systems based on the model decomposition. The proposed algorithm has lower computational cost than the...
in determining the performance of the k-nearest neighbors (KNN) algorithm. It functions as the primary tuning parameter, profoundly influencing the efficacy of the KNN model70. Bootstrap procedure is used to determine parameter k. The graphical presentation of KNN algorithm is illustrated in Fig.5...