Orthogonal Least Squares Combined with Regularisation
Automatic kernel regression modelling using combined leave one out test score and regularised orthogonal least squares
... parameter regularisation and robust model structure selection are effective and complementary approaches for robust modelling, it is highly desirable to develop algorithms by combining parameter ...
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Orthogonal least squares regression with tunable kernels
... A novel technique is proposed to construct sparse regression models based on the orthogonal least squares method with tunable kernels. The proposed technique tunes the centre vector and diagonal ...
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Local Regularization Assisted Orthogonal Least Squares Regression
... ½rerr l will not continuously decreases as more terms are added. This is because those unnecessarily added terms will have very large l l associated with them, effectively forcing their weights to be zero. Thus, when to ...
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Joint k-step analysis of Orthogonal Matching Pursuit and Orthogonal Least Squares
... Optimized Orthogonal Matching Pursuit (OOMP) [9] in the signal processing literature, all these algorithms being actually the ...an orthogonal projection methodol- ogy [5], or both [8, ...the ...
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Combined genetic algorithm optimization and regularized orthogonal least squares learning for radial basis function networks
... We propose a two-level learning hierarchy for construct- ing RBF networks based on the combined GA and ROLS algorithms. Because the generalization performance is a com- plex multimodal function on the space of the ...
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Least-Squares Adaptive Control Using Chebyshev Orthogonal Polynomials
... the least-squares gradient method, RLS with η = ...the least-squares gradient method maintains a very good tracking performance even with a 60-ms time ...the least-squares ...
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Orthogonal basis functions in discrete least-squares rational approximation
... We intend however to give the explicit relation with the approach by OVPs that was summarized above. This will show that, at least in the scalar case, the present approach is conceptually simpler. The idea is ...
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Sparse kernel regression modelling using combined locally regularized orthogonal least squares and D optimality experimental design
... regularized orthogonal least squares (LROLS) model selection with a D-optimality experimental ...the combined model selecting criterion and the entire model construction procedure becomes ...
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A multiple sequential orthogonal least squares algorithm for feature ranking and subset selection
... Principal component analysis is a useful tool for dimensionality reduction. Principal components, however, suffer from two main deficiencies: Principal components always involve all the original variables and are usually ...
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Tree Based Orthogonal Least Squares Regression with Repeated Weighted Boosting Search
... V. C ONCLUSIONS In order to a sparse representation, this paper proposes a novel tree based orthogonal least squares regression. Unlike most of the conventional OLSR, the new method keeps the k ...
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Finite impulse response filter design using a forward orthogonal least squares algorithm
... standard least squares problem. Compared with simple least squares algorithms, the orthogonal least squares method has been demonstrated to be a powerful means to achieve ...
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Combined l1 and Greedy l0 Penalized Least Squares for Linear Model Selection
... of an experimental matrix named irrepresentable conditions which are rather unlikely to hold in practice (Meinshausen and B¨ uhlmann, 2006; Zhao and Yu, 2006). However, consistency of the Lasso predictors or consistency ...
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Development of adaptive p-step RBF network model with recursive orthogonal least squares training
... Abstract. An adaptive 𝑝 -step prediction model for nonlinear dynamic processes is developed in this paper, and implemented with a radial basis function (RBF) network. The model can predict output for multi-step ahead ...
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On the Equivalence of the Weighted Least Squares and the Generalised Least Squares Estimators, with Applications to Kernel Smoothing
... It is evident that the linear system (20) is redundant: the last h rows of the complete matrix [K|i] can be deleted thus giving rise to a system of h + 1 equations in 2h + 1 unknown variables, let us denote it by K † σ = ...
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On the Equivalence of the Weighted Least Squares and the Generalised Least Squares Estimators, with Applications to Kernel Smoothing
... . It is evident that the linear system (20) is redundant: the last h rows of the complete matrix [ K |i] can be deleted thus giving rise to a system of h + 1 equations in 2h + 1 unknown variables, let us denote it by K † ...
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Approximate Least Squares Accelerator
... 66 Figure 47. 16x16 experiment for the 2's complement representation. 3.3. Comparison conclusions. Overall, the truncation methods perform well compared to the other novel methods. For certain dis- tributions other ...
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Least Squares Percentage Regression
... TYPE I ERROR RATES IN BEST SUBSET REGRESSION 406 Conclusion A computer based heuristic that uses randomization has been described. The algorithm allows control of Type I error rate for the overall statistical ...
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4.3 Least Squares Approximations
... Main point: Now A T A is diagonal. We can solve separately for C D 7 3 and D D 6 8 . The zeros in A T A are dot products of perpendicular columns in A. The diagonal matrix A T A, with entries m D 3 and t 1 2 C t 2 2 C t ...
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THE METHOD OF LEAST SQUARES THE METHOD OF LEAST SQUARES
... LINEAR REGRESSION LINEAR REGRESSION is a powerfull tool for studying fundamental relationships between two (or more) RVs Y and X. The method is based on the method of least squares. Let’s discuss the ...
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