Pages that link to "Item:Q1887129"

The following pages link to Practical selection of SVM parameters and noise estimation for SVM regression (Q1887129):

Displaying 50 items.

• Another look at statistical learning theory and regularization (Q280403) (← links)
• Modeling, forecasting and trading the EUR exchange rates with hybrid rolling genetic algorithms -- support vector regression forecast combinations (Q320100) (← links)
• Generalization ability of fractional polynomial models (Q461189) (← links)
• Adaptive support vector regression for UAV flight control (Q553250) (← links)
• Hybrid evolutionary algorithms in a SVR traffic flow forecasting model (Q632928) (← links)
• On selection of kernel parametes in relevance vector machines for hydrologic applications (Q732723) (← links)
• Global resolution of the support vector machine regression parameters selection problem with LPCC (Q748597) (← links)
• Noise model based \(\nu\)-support vector regression with its application to short-term wind speed forecasting (Q889277) (← links)
• Sparse deconvolution using support vector machines (Q966912) (← links)
• Electric load forecasting by support vector model (Q967915) (← links)
• Trade-off between accuracy and interpretability: experience-oriented fuzzy modeling via reduced-set vectors (Q971561) (← links)
• Recursive reduced least squares support vector regression (Q1010090) (← links)
• Data-based modeling and prediction of cytotoxicity induced by contaminants in water resources (Q1629369) (← links)
• Modeling of EDM responses by support vector machine regression with parameters selected by particle swarm optimization (Q1630717) (← links)
• Predicting specific surface areas of layered double hydroxides based on support vector regression integrated with a residual bootstrapping method (Q1649174) (← links)
• Applying two-stage neural network based classifiers to the identification of mixture control chart patterns for an SPC-EPC process (Q1687402) (← links)
• Reverse adaptive krill herd locally weighted support vector regression for forecasting and trading exchange traded funds (Q1694930) (← links)
• A forecast model of the number of containers for containership voyage (Q1712052) (← links)
• Fault isolation for nonlinear systems using flexible support vector regression (Q1719006) (← links)
• Planning of electric power systems considering virtual power plants with dispatchable loads included: an inexact two-stage stochastic linear programming model (Q1721331) (← links)
• Integrated use of statistical-based approaches and computational intelligence techniques for tumors classification using microarray (Q1723248) (← links)
• Volatility forecasting via SVR-GARCH with mixture of Gaussian kernels (Q1789603) (← links)
• Application of seasonal SVR with chaotic gravitational search algorithm in electricity forecasting (Q1792298) (← links)
• Experimentally optimal \(\nu\) in support vector regression for different noise models and parameter settings (Q1887132) (← links)
• An efficient and robust adaptive sampling method for polynomial chaos expansion in sparse Bayesian learning framework (Q1988073) (← links)
• Bat algorithm assisted by ordinal optimization for solving discrete probabilistic bicriteria optimization problems (Q1997713) (← links)
• A hybrid regression model for water quality prediction (Q2009204) (← links)
• A new adaptive LSSVR with online multikernel RBF tuning to evaluate analog circuit performance (Q2015243) (← links)
• Approximate dynamic programming for the military aeromedical evacuation dispatching, preemption-rerouting, and redeployment problem (Q2029316) (← links)
• Sparse regression for extreme values (Q2074318) (← links)
• Geometric insights into support vector machine behavior using the KKT conditions (Q2074327) (← links)
• A novel quality prediction method based on feature selection considering high dimensional product quality data (Q2086964) (← links)
• Deep learning for gas sensing using MOFs coated weakly-coupled microbeams (Q2109926) (← links)
• Robust regression using support vector regressions (Q2131669) (← links)
• Inter-class sparsity based discriminative least square regression (Q2179822) (← links)
• Machine learning-based surrogate modeling for data-driven optimization: a comparison of subset selection for regression techniques (Q2182781) (← links)
• Use of support vector regression in structural optimization: application to vehicle crashworthiness design (Q2225142) (← links)
• An e-E-insensitive support vector regression machine (Q2259798) (← links)
• A multi-loss super regression learner (MSRL) with application to survival prediction using proteomics (Q2259821) (← links)
• Forecasting government bond spreads with heuristic models: evidence from the eurozone periphery (Q2288926) (← links)
• Maximum likelihood optimal and robust support vector regression with \textit{lncosh} loss function (Q2292212) (← links)
• High-precision combined tidal forecasting model (Q2312431) (← links)
• Efficient global optimization algorithm assisted by multiple surrogate techniques (Q2392755) (← links)
• Extended support vector interval regression networks for interval input-output data (Q2466086) (← links)
• Real-time prediction of order flowtimes using support vector regression (Q2483499) (← links)
• On a strategy to develop robust and simple tariffs from motor vehicle insurance data (Q2508010) (← links)
• Applying nonlinear generalized autoregressive conditional heteroscedasticity to compensate ANFIS outputs tuned by adaptive support vector regression (Q2508924) (← links)
• Theoretically optimal parameter choices for support vector regression machines with noisy input (Q2576617) (← links)
• Predictive modeling in a steelmaking process using optimized relevance vector regression and support vector regression (Q2675703) (← links)
• An innovative approach of determining the sample data size for machine learning models: a case study on health and safety management for infrastructure workers (Q2700280) (← links)