[PDF] Top 20 Model Selection in Kernel Based Regression using the Influence Function
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Model Selection in Kernel Based Regression using the Influence Function
... that using a Huber loss instead of least squares gives similar results except for the Boston housing data, Friedman 1 and especially Friedman ...loss function and the error ...loss function can ... See full document
24
Kernel Partial Least Squares Regression in Reproducing Kernel Hilbert Space
... the regression coefficients that are too large in absolute ...estimated regression coefficients may change considerably given different data ...a regression model which fits the training data ... See full document
27
Parsimonious least squares support vector regression using orthogonal forward selection with the generalised kernel model
... Forward selection using the Orthogonal Least Squares (OLS) algorithm (Chen et ...sparse kernel modelling techniques, such as the Support Vector Machine (SVM) (Chapelle et ...In regression, the ... See full document
12
SECURE ROUTING IN MANET USING ASYMMETRIC GRAPHS
... vector regression we are mainly concerned with the optimization of insensitive loss function ε , penalty coefficient C , and γ in kernel function K x x ( , i j ) , which are decisive to the ... See full document
6
Probability density function estimation using orthogonal forward regression
... Abstract— Using the classical Parzen window estimate as the target function, the kernel density estimation is formulated as a regression problem and the orthogonal forward regression ... See full document
6
Learning with the Maximum Correntropy Criterion Induced Losses for Regression
... the regression function. Debruyne et al. (2008) addressed the model selection problem in kernel-based robust ...of regression schemes associated with convex robust loss ... See full document
42
Bootstrap based model selection in subset polynomial regression
... polynomial regression model is a polynomial regression in which some regression coefficients have a zero ...this model is the user can select a regression model from all ... See full document
8
The Prediction Model of Financial Crisis Based on the Combination of Principle Component Analysis and Support Vector Machine
... SVM model to predi- cate financial crisis, they focus on the selection of kernel function and financial index, and few researchers use Principal Component Analysis (PCA) method to extract ... See full document
9
An Exponential Kernel based Fuzzy Rough Sets Model for Feature Selection
... of kernel methods. Kernel methods allows mapping the data into a high dimensional feature space in order to increase the computation of linear learning algorithms ...[3]. Kernel defines a similarity ... See full document
8
Variable Selection Using SVM based Criteria (Kernel Machines Section)
... Gaussian kernel parameter σ, degree d of a poly- nomial kernel, slack variables penalization C ) that have to be tuned to achieve the best general- ization ...feature selection algorithm, these ... See full document
14
Nonlinear Regression Estimation Using Subset-Based Kernel Principal Components
... linear regression and kernel ridge ...mean function based on a multivariate nonlinear regression ...performance based on a multivariate nonlinear time series ... See full document
36
Sparse support vector regression based on orthogonal forward selection for the generalised kernel model
... loss function and the squared weights of the ...‘‘generalised’’ kernel function is used in which each kernel regressor has its tunable centre vector and diagonal covariance ...forward ... See full document
14
Model Selection for Regression with Continuous Kernel Functions Using the Modulus of Continuity
... target function were ...the model selection using the AIC, BIC, MDL, and MCIC based methods are presented in Tables 2 and ...variances using (7) were ... See full document
27
Parsimonious support vector machine regression using orthogonal forward selection with the generalized kernel model
... Forward selection using the orthogonal least squares (OLS) algorithm [1] is a simple and efficient construction method that is capable of producing parsimonious linear-in-the-weights nonlinear models with ... See full document
9
Support vector machine modeling of earthquake-induced landslides susceptibility in central part of Sichuan province, China
... four kernel functions: linear, polynomial, radial basis function and ...validated using area-under-curve (AUC) analysis of success- rate curves and prediction-rate ...basis function suitably ... See full document
12
Finding kernel function for stock market prediction with support vector regression
... From the theoretical framework, it is clear that the input data consisted of a series of past End Of Day stock market data obtained from Kuala Lumpur Stock Exchange. These data will go through some pre-processing process ... See full document
56
Nonparametric and semiparametric regression model selection
... before using model (1.1) one needs to con- sider a model selection ...linear model case, the conventional nonparametric cross- validation model selection function ... See full document
39
Using Document Summarization Techniques for Speech Data Subset Selection
... priori selection of a data set before (re-)training a system; in this case the goal is to subselect the existing data set as well as possible, eliminating redundant infor- mation; (b) selection for ... See full document
6
Anomaly detection using local kernel density estimation and context based regression
... local kernel regression estimator and a hierarchical combination strategy to combine the information from the multiple scale neighborhoods for both locally and globally refining the anomaly ...a ... See full document
34
An improved moth flame optimization algorithm based on rough sets for tomato diseases detection
... in each class Feature selection method Accuracy Precision Recall 1 (2*100,2*100) MFORS-KNN with k=1 84% 84.2% 84% 3 (2*100,2*100) MFORS-KNN with k=1 85% 85.1% 85% 5 (2*100,2*100) MFORS-KNN with k=1 87% 87.1% 87% 7 ... See full document
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