[PDF] Top 20 Sparse support vector regression based on orthogonal forward selection for the generalised kernel model
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Sparse support vector regression based on orthogonal forward selection for the generalised kernel model
... considers sparse regression modelling using a generalised kernel model in which each kernel regressor has its individually tuned centre vector and diagonal covariance ... See full document
14
Parsimonious least squares support vector regression using orthogonal forward selection with the generalised kernel model
... reproducing kernel Hilbert space and can be applied to non-Mercer ...a sparse kernel model construction algorithm, called the LS-SESVM, has been ...OLS forward selection ... See full document
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Sparse modelling using orthogonal forward regression with PRESS statistic and regularization
... the model simply fits the training data well. Rather, the goodness of a model is characterized by its generalization capability, inter- pretability and ease for knowledge ...priate sparse models by ... See full document
14
Sparse kernel density construction using orthogonal forward regression with leave one out test score and local regularization
... obtaining sparse kernel density estimates based on a regression approach that directly optimizes model generalization ...an orthogonal forward regression, and the ... See full document
10
Sparse kernel density construction using orthogonal forward regression with leave one out test score and local regularization
... obtaining sparse kernel density estimates based on a regression approach that directly optimizes model generalization ...an orthogonal forward regression, and the ... See full document
10
A kernel based two class classifier for imbalanced data sets
... on kernel-model-based construction/selection approached, such as support vector machine (SVM), relevance vector machine (RVM), and orthogonal forward ... See full document
14
A sparse kernel density estimation algorithm using forward constrained regression
... mixture model [5]. The finite mixture model includes the well known PW estimate [4] as a special ...mixture model with the capability to infer a minimal number of mixtures from the data ...into ... See full document
10
Orthogonal Forward Regression based on Directly Maximizing Model Generalization Capability
... for sparse kernel modelling using the leave-one-out test score also known as the PRESS (Predicted REsidual Sums of Squares) ...subset model selection procedure is developed in the ... See full document
6
Probability density function estimation using orthogonal forward regression
... (PDF) based on a realisation sample drawn from the underlying density is based on a non-parametric approach ...the kernel density estimate, employs the full data sample set in defining density ... See full document
6
Dimensionality Reduction via Sparse Support Vector Machines (Kernel Machines Section)
... individual model can be considered completely reliable, especially for QSAR ...constructed based on T = 20 random partitions to produce distinct weight ...“bagged” model (Breiman, 1996). For problems ... See full document
15
Sparse model identification using orthogonal forward regression with basis pursuit and D optimality
... and support vector machines (SVM)) have been extensively studied [1 – ...lattice- based networks or knowledge representations such as fuzzy logic (FL), RBF, Karneva distributed memory maps, and all ... See full document
8
Parsimonious support vector machine regression using orthogonal forward selection with the generalized kernel model
... standard kernel regression modelling (both of SVM and OLS), each kernel regressor is positioned at a training input data point and a single common kernel variance is used for every ...OLS ... See full document
9
Variable selection using least absolute shrinkage and selection operator
... Variable selection is the general method in selecting the k independent variable p to form a simpler model to explain Y such as ...variable selection methods in statistic, such as forward ... See full document
21
Local Regularization Assisted Orthogonal Least Squares Regression
... final model had 30 terms. The resulting phase plot of this 30-term model generated iteratively is depicted in ...LROLS selection procedure, after k had converged (10 iterations), is listed in Table ... See full document
27
Finding kernel function for stock market prediction with support vector regression
... lastly support vector machine and artificial neural ...of support vector machine structure that includes data prepossessing and parameter selection, experiments on the prepared data, ... See full document
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Kernel Sparse Coding & Texture Feature based Segmentation for Cerebral Edema
... the kernel Sparse Coding (KSR) for more clarification and spotting the tumours and then we used the texture feature which is the key and also the supporting feature for the KSR ... See full document
5
An iterative orthogonal forward regression algorithm
... input) model and the associated Orthogonal Forward Regression (OFR) algorithm have been widely applied in nonlinear system identification including in the modelling of many engineering, ... See full document
28
Forecasting the geomagnetic activity of the Dst Index using radial basis function networks
... Several model types can be used to approximate the nonlinear function f and different model types often involve totally different training/learning ... See full document
17
Automatic kernel regression modelling using combined leave one out test score and regularised orthogonal least squares
... regularised orthogonal least ...maximised model robustness via two effective and complementary approaches, parameter regularisation via ridge regression and model optimal generalisation ... See full document
18
Heart Disease Prediction and Performance Assessment through Attribute Element Diminution using Machine Learning
... The health care management assembles and collects the large amount of data with hidden and secured information. The effective decision and predictions are taken; advanced data mining techniques are used. An effective ... See full document
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