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Extended Kernel Recursive Least Squares Algorithm
"... This paper presents a kernelized version of the extended recursive least squares (EXKRLS) algorithm which implements for the first time a general linear state model in reproducing kernel Hilbert spaces (RKHS), or equivalently a general nonlinear state model in the input space. The center piece of t ..."
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Cited by 17 (3 self)
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This paper presents a kernelized version of the extended recursive least squares (EXKRLS) algorithm which implements for the first time a general linear state model in reproducing kernel Hilbert spaces (RKHS), or equivalently a general nonlinear state model in the input space. The center piece
Square Root Extended Kernel Recursive Least Squares Algorithm for Nonlinear Channel Equalization
, 2013
"... Abstract: This study presents a square root version of extended kernel recursive least square algorithm. Basically main idea is to overcome the divergence phenomena arise in the computation of weights of the extended kernel recursive least squares algorithm. Numerically stable givens orthogonal tra ..."
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Abstract: This study presents a square root version of extended kernel recursive least square algorithm. Basically main idea is to overcome the divergence phenomena arise in the computation of weights of the extended kernel recursive least squares algorithm. Numerically stable givens orthogonal
Benchmarking Least Squares Support Vector Machine Classifiers
 NEURAL PROCESSING LETTERS
, 2001
"... In Support Vector Machines (SVMs), the solution of the classification problem is characterized by a (convex) quadratic programming (QP) problem. In a modified version of SVMs, called Least Squares SVM classifiers (LSSVMs), a least squares cost function is proposed so as to obtain a linear set of eq ..."
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Cited by 476 (46 self)
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In Support Vector Machines (SVMs), the solution of the classification problem is characterized by a (convex) quadratic programming (QP) problem. In a modified version of SVMs, called Least Squares SVM classifiers (LSSVMs), a least squares cost function is proposed so as to obtain a linear set
The Kernel Recursive Least Squares Algorithm
 IEEE Transactions on Signal Processing
, 2003
"... We present a nonlinear kernelbased version of the Recursive Least Squares (RLS) algorithm. Our KernelRLS (KRLS) algorithm performs linear regression in the feature space induced by a Mercer kernel, and can therefore be used to recursively construct the minimum mean squared error regressor. Spars ..."
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Cited by 141 (2 self)
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We present a nonlinear kernelbased version of the Recursive Least Squares (RLS) algorithm. Our KernelRLS (KRLS) algorithm performs linear regression in the feature space induced by a Mercer kernel, and can therefore be used to recursively construct the minimum mean squared error regressor
EXTENDED RECURSIVE LEAST SQUARES IN RKHS
"... In this paper, a kernelized version of the extended recursive least squares (ExRLS) algorithm, along with its Kalman filter interpretation will be presented. The center piece of this development is a reformulation of the ExRLS algorithm which only requires inner product operations between input ve ..."
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In this paper, a kernelized version of the extended recursive least squares (ExRLS) algorithm, along with its Kalman filter interpretation will be presented. The center piece of this development is a reformulation of the ExRLS algorithm which only requires inner product operations between input
Manifold regularization: A geometric framework for learning from labeled and unlabeled examples
 JOURNAL OF MACHINE LEARNING RESEARCH
, 2006
"... We propose a family of learning algorithms based on a new form of regularization that allows us to exploit the geometry of the marginal distribution. We focus on a semisupervised framework that incorporates labeled and unlabeled data in a generalpurpose learner. Some transductive graph learning al ..."
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Cited by 578 (16 self)
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algorithms and standard methods including Support Vector Machines and Regularized Least Squares can be obtained as special cases. We utilize properties of Reproducing Kernel Hilbert spaces to prove new Representer theorems that provide theoretical basis for the algorithms. As a result (in contrast to purely
Kernel Recursive Least Squares
 IEEE Transactions on Signal Processing
, 2004
"... We present a nonlinear kernelbased version of the Recursive Least Squares (RLS) algorithm. Our KernelRLS algorithm performs linear regression in the feature space induced by a Mercer kernel, and can therefore be used to recursively construct the minimum meansquared error regressor. Sparsity (and ..."
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Cited by 13 (0 self)
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We present a nonlinear kernelbased version of the Recursive Least Squares (RLS) algorithm. Our KernelRLS algorithm performs linear regression in the feature space induced by a Mercer kernel, and can therefore be used to recursively construct the minimum meansquared error regressor. Sparsity
The Kernel Recursive LeastSquares Algorithm
"... Abstract—We present a nonlinear version of the recursive least squares (RLS) algorithm. Our algorithm performs linear regression in a highdimensional feature space induced by a Mercer kernel and can therefore be used to recursively construct minimum meansquarederror solutions to nonlinear leasts ..."
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Abstract—We present a nonlinear version of the recursive least squares (RLS) algorithm. Our algorithm performs linear regression in a highdimensional feature space induced by a Mercer kernel and can therefore be used to recursively construct minimum meansquarederror solutions to nonlinear leastsquares
Extended Kalman Filter Using a Kernel Recursive Least Squares Observer
"... Abstract — In this paper, a novel methodology is proposed to solve the state estimation problem combining the extended Kalman filter (EKF) with a kernel recursive least squares (KRLS) algorithm (EKFKRLS). The EKF algorithm estimates hidden states in the input space, while the KRLS algorithm estimat ..."
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Abstract — In this paper, a novel methodology is proposed to solve the state estimation problem combining the extended Kalman filter (EKF) with a kernel recursive least squares (KRLS) algorithm (EKFKRLS). The EKF algorithm estimates hidden states in the input space, while the KRLS algorithm
Linear leastsquares algorithms for temporal difference learning
 Machine Learning
, 1996
"... Abstract. We introduce two new temporal difference (TD) algorithms based on the theory of linear leastsquares function approximation. We define an algorithm we call LeastSquares TD (LS TD) for which we prove probabilityone convergence when it is used with a function approximator linear in the adju ..."
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Cited by 260 (1 self)
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in the adjustable parameters. We then define a recursive version of this algorithm, Recursive LeastSquares TD (RLS TD). Although these new TD algorithms require more computation per timestep than do Sutton's TD(A) algorithms, they are more efficient in a statistical sense because they extract more
Results 1  10
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