[PDF] Top 20 Local Regularization Assisted Orthogonal Least Squares Regression
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Local Regularization Assisted Orthogonal Least Squares Regression
... specific regularization scheme is commented. Different regularization schemes can be interpreted as different choices of prior in Bayesian ...updating regularization parameters in the LROLS algorithm ... See full document
27
Sparse modelling using orthogonal forward regression with PRESS statistic and regularization
... structure regularization by using a penalty term to penalize large-sized ...i.e., local regularization [9], [13], [15], and optimal experimental design criteria [14] offer better solutions as model ... See full document
14
Sparse kernel regression modelling using combined locally regularized orthogonal least squares and D optimality experimental design
... structure regularization by conditioning the model size using a penalty term to penalize large sized ...forward regression only affects the stopping point of the model selection, but does not penalizes the ... See full document
8
Orthogonal Forward Regression based on Directly Maximizing Model Generalization Capability
... The least squares (LS) principle has been fundamental to data modelling and the training mean square error (MSE) has always played a central role in model structure con- struction and parameter ...[1], ... See full document
6
Sparse kernel density construction using orthogonal forward regression with leave one out test score and local regularization
... an orthogonal forward regression (OFR) that incrementally minimizes the training mean square error (MSE) ...and local regularization. Specifically, we extend the regression model ... See full document
10
Regularized orthogonal least squares algorithm for constructing radial basis function networks
... The proposed algorithm combines the advantages of both the orthogonal forward regression and regularization methods to provide an efficient and powerful procedure for constructing parsim[r] ... See full document
10
Model structure selection in powertrain calibration and control
... as local models which can be developed using multiple inputs and outputs (u1: air bleed valve duty, u2: manifold air pressure, u3: engine speed, u4: fuel pulse width, y1: torque, y2: air/fuel ...the local ... See full document
158
Sparse kernel density construction using orthogonal forward regression with leave one out test score and local regularization
... the orthogonal least squares algorithm [21], [22], as is first shown in [20], and multiple-regularizers or local regulariza- tion is known to be capable of providing very sparse solutions [8], ... See full document
10
Ultra-Orthogonal Forward Regression Algorithms for the Identification of Non-Linear Dynamic Systems
... (iterative Orthogonal Forward Regression) algorithm has therefore been proposed to reduce these problems while maintaining the simplicity of the identification ...original least squares ... See full document
25
Combined genetic algorithm optimization and regularized orthogonal least squares learning for radial basis function networks
... the regularization parameter and width, it is best to examine the generalization performance as a function of these two ...a local optimal value of and ... See full document
5
Parsimonious least squares support vector regression using orthogonal forward selection with the generalised kernel model
... kernel regression modelling approach has the potential of improving modelling capability and producing sparser final models, compared with the standard approach of restricting the kernel centres to the training ... See full document
12
On the Consistency of Feature Selection using Greedy Least Squares Regression
... Moreover, if the eigenvalue ρ( F ¯ ) is bounded away from zero, then the greedy algorithm can reliably identify features as long as each nonzero coefficient is larger than a constant times the noise level. In comparison, ... See full document
14
A Risk Comparison of Ordinary Least Squares vs Ridge Regression
... OLS regression and used it as an estimate of the true parameter ...ridge regression (RR) and PCA-OLS models on a small subset of these ...ridge regression (RR) and PCA-OLS ... See full document
7
An Orthogonal Forward Regression Algorithm Combined with Basis Pursuit and D Optimality
... eters. Both parameter regularization and basis pursuit can be integrated into a Bayesian framework [12, 13, 16]. The advantage of the basis pursuit is that it can achieve much sparser models by forcing more ... See full document
6
A multiple sequential orthogonal least squares algorithm for feature ranking and subset selection
... encountered in multiple regression and multivariate pattern recognition. It has been noted that in many cases not all the original variables are necessary for characterizing the overall features. More often only a ... See full document
18
Tree Based Orthogonal Least Squares Regression with Repeated Weighted Boosting Search
... Abstract—Orthogonal Least Squares Regression (OLSR) selects each regressor by repeated weighted boosting search ...sparser regression model in the framework of OLSR with ...each ... See full document
9
Harder, Better, Faster, Stronger Convergence Rates for Least-Squares Regression
... corresponds up to a constant factor to the bias term of regularized least-squares (Hsu et al., 2014), but it is achieved by an algorithm accessing only n stochastic gradients. Note that when λ or γ depend ... See full document
51
Speech Emotion Recognition with MPCA and Kernel Partial Least Squares Regression
... Abstract—Speech signal is one of the major means for communication which carries not only semantic, but personal information , such as genders and emotions. The researches about speech emotion have become more and more ... See full document
7
Comparing least-squares and goal programming estimates of linear regression parameter.
... [r] ... See full document
12
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 ... See full document
5
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